",isbn:"978-1-83969-452-3",printIsbn:"978-1-83969-451-6",pdfIsbn:"978-1-83969-453-0",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"a6e1a11c05ff8853c529750ddfac6c11",bookSignature:"Dr. René Mauricio Barría",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10734.jpg",keywords:"Neonatal Intensive Unit, Neonatal Diagnostic Techniques, Neonatal Nurses, Neonatologists, Newborn Diseases, Premature Diseases, Breast Feeding, Kangaroo-Mother Care Method, Neonatal Survival, Limit of Viability, Minimal Handling, Neonatal Stress",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 5th 2021",dateEndSecondStepPublish:"March 5th 2021",dateEndThirdStepPublish:"May 4th 2021",dateEndFourthStepPublish:"July 23rd 2021",dateEndFifthStepPublish:"September 21st 2021",remainingDaysToSecondStep:"2 days",secondStepPassed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"The principal investigator and academic expert in epidemiological methods and evidence-based health with an emphasis on children's health. His research interests lie in the areas of Maternal-Child Health, Neonatal Care, and Environmental Health. From 2010 until 2017 he was Director of the Evidence-Based Health Office and currently serves as Director of the Nursing Institute at the Universidad Austral de Chile.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"88861",title:"Dr.",name:"R. Mauricio",middleName:null,surname:"Barría",slug:"r.-mauricio-barria",fullName:"R. Mauricio Barría",profilePictureURL:"https://mts.intechopen.com/storage/users/88861/images/system/88861.jpg",biography:"R. Mauricio Barría, DrPH, is a Principal Investigator and Associate Professor at the Faculty of Medicine at Universidad Austral de Chile. He was trained as an epidemiologist and received his MSc in Clinical Epidemiology from Universidad de la Frontera in Temuco, Chile, and his DrPH from Universidad de Chile in Santiago, Chile. His research interests lie in the areas of Maternal-Child Health, Neonatal Care and Environmental Health. He is skilled in epidemiological studies designs with special interest in cohort studies and clinical trials. Since 2010 until 2017 he was Director of the Evidence-Based Health Office and currently serves as Director of the Nursing Institute at the Universidad Austral de Chile. He has published several articles related to the care and health of the newborn and is a reviewer of several international journals.",institutionString:"Austral University of Chile",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"4",institution:{name:"Austral University of Chile",institutionURL:null,country:{name:"Chile"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"345821",firstName:"Darko",lastName:"Hrvojic",middleName:null,title:"Mr.",imageUrl:"//cdnintech.com/web/frontend/www/assets/author.svg",email:"darko@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. Mauricio Barría",coverURL:"https://cdn.intechopen.com/books/images_new/6550.jpg",editedByType:"Edited by",editors:[{id:"88861",title:"Dr.",name:"R. Mauricio",surname:"Barría",slug:"r.-mauricio-barria",fullName:"R. Mauricio Barría"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5821",title:"Selected Topics in Neonatal Care",subtitle:null,isOpenForSubmission:!1,hash:"711594f833d5470b73524758472f4d96",slug:"selected-topics-in-neonatal-care",bookSignature:"R. Mauricio Barría",coverURL:"https://cdn.intechopen.com/books/images_new/5821.jpg",editedByType:"Edited by",editors:[{id:"88861",title:"Dr.",name:"R. Mauricio",surname:"Barría",slug:"r.-mauricio-barria",fullName:"R. Mauricio Barría"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8728",title:"Update on Critical Issues on Infant and Neonatal Care",subtitle:null,isOpenForSubmission:!1,hash:"52c4dbe7c0deb54899657dc4323238d6",slug:"update-on-critical-issues-on-infant-and-neonatal-care",bookSignature:"René Mauricio Barría",coverURL:"https://cdn.intechopen.com/books/images_new/8728.jpg",editedByType:"Edited by",editors:[{id:"88861",title:"Dr.",name:"R. Mauricio",surname:"Barría",slug:"r.-mauricio-barria",fullName:"R. Mauricio Barría"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6191",title:"Selected Topics in Breastfeeding",subtitle:null,isOpenForSubmission:!1,hash:"3334b831761ffa52e78de6fc681e33b3",slug:"selected-topics-in-breastfeeding",bookSignature:"R. Mauricio Barría P.",coverURL:"https://cdn.intechopen.com/books/images_new/6191.jpg",editedByType:"Edited by",editors:[{id:"88861",title:"Dr.",name:"R. Mauricio",surname:"Barría",slug:"r.-mauricio-barria",fullName:"R. Mauricio Barría"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"63970",title:"Some Commonly Used Speech Feature Extraction Algorithms",doi:"10.5772/intechopen.80419",slug:"some-commonly-used-speech-feature-extraction-algorithms",body:'\n
\n
1. Introduction
\n
Human beings express their feelings, opinions, views and notions orally through speech. The speech production process includes articulation, voice, and fluency [1, 2]. It is a complex naturally acquired human motor abilities, a task categorized in regular adults by the production of about 14 different sounds per second via the harmonized actions of roughly 100 muscles connected by spinal and cranial nerves. The simplicity with which human beings speak is in contrast to the complexity of the task, and that complexity could assist in explaining why speech can be very sensitive to diseases associated with the nervous system [3].
\n
There have been several successful attempts in the development of systems that can analyze, classify and recognize speech signals. Both hardware and software that have been developed for such tasks have been applied in various fields such as health care, government sectors and agriculture. Speaker recognition is the capability of a software or hardware to receive speech signal, identify the speaker present in the speech signal and recognize the speaker afterwards [4]. Speaker recognition executes a task similar to what the human brain undertakes. This starts from speech which is an input to the speaker recognition system. Generally, speaker recognition process takes place in three main steps which are acoustic processing, feature extraction and classification/recognition [5].
\n
The speech signal has to be processed to remove noise before the extraction of the important attributes in the speech [6] and identification. The purpose of feature extraction is to illustrate a speech signal by a predetermined number of components of the signal. This is because all the information in the acoustic signal is too cumbersome to deal with, and some of the information is irrelevant in the identification task [7, 8].
\n
Feature extraction is accomplished by changing the speech waveform to a form of parametric representation at a relatively lesser data rate for subsequent processing and analysis. This is usually called the front end signal-processing [9, 10]. It transforms the processed speech signal to a concise but logical representation that is more discriminative and reliable than the actual signal. With front end being the initial element in the sequence, the quality of the subsequent features (pattern matching and speaker modeling) is significantly affected by the quality of the front end [10].
\n
Therefore, acceptable classification is derived from excellent and quality features. In present automatic speaker recognition (ASR) systems, the procedure for feature extraction has normally been to discover a representation that is comparatively reliable for several conditions of the same speech signal, even with alterations in the environmental conditions or speaker, while retaining the portion that characterizes the information in the speech signal [7, 8].
\n
Feature extraction approaches usually yield a multidimensional feature vector for every speech signal [11]. A wide range of options are available to parametrically represent the speech signal for the recognition process, such as perceptual linear prediction (PLP), linear prediction coding (LPC) and mel-frequency cepstrum coefficients (MFCC). MFCC is the best known and very popular [9, 12]. Feature extraction is the most relevant portion of speaker recognition. Features of speech have a vital part in the segregation of a speaker from others [13]. Feature extraction reduces the magnitude of the speech signal devoid of causing any damage to the power of speech signal [14].
\n
Before the features are extracted, there are sequences of preprocessing phases that are first carried out. The preprocessing step is pre-emphasis. This is achieved by passing the signal through a FIR filter [15] which is usually a first-order finite impulse response (FIR) filter [16]. This is succeeded by frame blocking, a method of partitioning the speech signal into frames. It removes the acoustic interface existing in the start and end of the speech signal [17].
\n
The framed speech signal is then windowed. Bandpass filter is a suitable window [15] that is applied to minimize disjointedness at the start and finish of each frame. The two most famous categories of windows are Hamming and Rectangular windows [18]. It increases the sharpness of harmonics, eliminates the discontinuous of signal by tapering beginning and ending of the frame zero. It also reduces the spectral distortion formed by the overlap [17].
\n
\n
\n
2. Mel frequency cepstral coefficients (MFCC)
\n
Mel frequency cepstral coefficients (MFCC) was originally suggested for identifying monosyllabic words in continuously spoken sentences but not for speaker identification. MFCC computation is a replication of the human hearing system intending to artificially implement the ear’s working principle with the assumption that the human ear is a reliable speaker recognizer [19]. MFCC features are rooted in the recognized discrepancy of the human ear’s critical bandwidths with frequency filters spaced linearly at low frequencies and logarithmically at high frequencies have been used to retain the phonetically vital properties of the speech signal. Speech signals commonly contain tones of varying frequencies, each tone with an actual frequency, f (Hz) and the subjective pitch is computed on the Mel scale. The mel-frequency scale has linear frequency spacing below 1000 Hz and logarithmic spacing above 1000 Hz. Pitch of 1 kHz tone and 40 dB above the perceptual audible threshold is defined as 1000 mels, and used as reference point [20].
\n
MFCC is based on signal disintegration with the help of a filter bank. The MFCC gives a discrete cosine transform (DCT) of a real logarithm of the short-term energy displayed on the Mel frequency scale [21]. MFCC is used to identify airline reservation, numbers spoken into a telephone and voice recognition system for security purpose. Some modifications have been proposed to the basic MFCC algorithm for better robustness, such as by lifting the log-mel-amplitudes to an appropriate power (around 2 or 3) before applying the DCT and reducing the impact of the low-energy parts [4].
\n
\n
2.1. Algorithm description, strength and weaknesses
\n
MFCC are cepstral coefficients derived on a twisted frequency scale centerd on human auditory perception. In the computation of MFCC, the first thing is windowing the speech signal to split the speech signal into frames. Since the high frequency formants process reduced amplitude compared to the low frequency formants, high frequencies are emphasized to obtain similar amplitude for all the formants. After windowing, Fast Fourier Transform (FFT) is applied to find the power spectrum of each frame. Subsequently, the filter bank processing is carried out on the power spectrum, using mel-scale. The DCT is applied to the speech signal after translating the power spectrum to log domain in order to calculate MFCC coefficients [5]. The formula used to calculate the mels for any frequency is [19, 22]:
where k is the number of mel cepstrum coefficients, \n\n\n\nS\n̂\n\nk\n\n\n is the output of filterbank and \n\n\n\nC\n̂\n\nn\n\n\n is the final mfcc coefficients.
\n
The block diagram of the MFCC processor can be seen in Figure 1. It summarizes all the processes and steps taken to obtain the needed coefficients. MFCC can effectively denote the low frequency region better than the high frequency region, henceforth, it can compute formants that are in the low frequency range and describe the vocal tract resonances. It has been generally recognized as a front-end procedure for typical Speaker Identification applications, as it has reduced vulnerability to noise disturbance, with minute session inconsistency and easy to mine [19]. Also, it is a perfect representation for sounds when the source characteristics are stable and consistent (music and speech) [23]. Furthermore, it can capture information from sampled signals with frequencies at a maximum of 5 kHz, which encapsulates most energy of sounds that are generated by humans [9].
\n
Figure 1.
Block diagram of MFCC processor.
\n
Cepstral coefficients are said to be accurate in certain pattern recognition problems relating to human voice. They are used extensively in speaker identification and speech recognition [21]. Other formants can also be above 1 kHz and are not efficiently taken into consideration by the large filter spacing in the high frequency range [19]. MFCC features are not exactly accurate in the existence of background noise [14, 24] and might not be well suited for generalization [23].
\n
\n
\n
\n
3. Linear prediction coefficients (LPC)
\n
Linear prediction coefficients (LPC) imitates the human vocal tract [16] and gives robust speech feature. It evaluates the speech signal by approximating the formants, getting rid of its effects from the speech signal and estimate the concentration and frequency of the left behind residue. The result states each sample of the signal as a direct incorporation of previous samples. The coefficients of the difference equation characterize the formants, thus, LPC needs to approximate these coefficients [25]. LPC is a powerful speech analysis method and it has gained fame as a formant estimation method [17].
\n
The frequencies where the resonant crests happen are called the formant frequencies. Thus, with this technique, the positions of the formants in a speech signal are predictable by calculating the linear predictive coefficients above a sliding window and finding the crests in the spectrum of the subsequent linear prediction filter [17]. LPC is helpful in the encoding of high quality speech at low bit rate [13, 26, 27].
\n
Other features that can be deduced from LPC are linear predication cepstral coefficients (LPCC), log area ratio (LAR), reflection coefficients (RC), line spectral frequencies (LSF) and Arcus Sine Coefficients (ARCSIN) [13]. LPC is generally used for speech reconstruction. LPC method is generally applied in musical and electrical firms for creating mobile robots, in telephone firms, tonal analysis of violins and other string musical gadgets [4].
\n
\n
3.1. Algorithm description, strength and weaknesses
\n
Linear prediction method is applied to obtain the filter coefficients equivalent to the vocal tract by reducing the mean square error in between the input speech and estimated speech [28]. Linear prediction analysis of speech signal forecasts any given speech sample at a specific period as a linear weighted aggregation of preceding samples. The linear predictive model of speech creation is given as [13, 25]:
Subsequently, each frame of the windowed signal is autocorrelated, while the highest autocorrelation value is the order of the linear prediction analysis. This is followed by the LPC analysis, where each frame of the autocorrelations is converted into LPC parameters set which consists of the LPC coefficients [26]. A summary of the procedure for obtaining the LPC is as seen in Figure 2. LPC can be derived by [7]:
where am is the linear prediction coefficient, km is the reflection coefficient.
\n
Linear predictive analysis efficiently selects the vocal tract information from a given speech [16]. It is known for the speed of computation and accuracy [18]. LPC excellently represents the source behaviors that are steady and consistent [23]. Furthermore, it is also be used in speaker recognition system where the main purpose is to extract the vocal tract properties [25]. It gives very accurate estimates of speech parameters and is comparatively efficient for computation [14, 26]. Traditional linear prediction suffers from aliased autocorrelation coefficients [29]. LPC estimates have high sensitivity to quantization noise [30] and might not be well suited for generalization [23].
\n
\n
\n
\n
4. Linear prediction cepstral coefficients (LPCC)
\n
Linear prediction cepstral coefficients (LPCC) are cepstral coefficients derived from LPC calculated spectral envelope [11]. LPCC are the coefficients of the Fourier transform illustration of the logarithmic magnitude spectrum [30, 31] of LPC. Cepstral analysis is commonly applied in the field of speech processing because of its ability to perfectly symbolize speech waveforms and characteristics with a limited size of features [31].
\n
It was observed by Rosenberg and Sambur that adjacent predictor coefficients are highly correlated and therefore, representations with less correlated features would be more efficient, LPCC is a typical example of such. The relationship between LPC and LPCC was originally derived by Atal in 1974. In theory, it is relatively easy to convert LPC to LPCC, in the case of minimum phase signals [32].
\n
\n
4.1. Algorithm description, strength and weaknesses
\n
In speech processing, LPCC analogous to LPC, are computed from sample points of a speech waveform, the horizontal axis is the time axis, while the vertical axis is the amplitude axis [31]. The LPCC processor is as seen in Figure 3. It pictorially explains the process of obtaining LPCC. LPCC can be calculated using [7, 15, 33]:
where \n\n\na\nm\n\n\n is the linear prediction coefficient, \n\n\nC\nm\n\n\n is the cepstral coefficient.
\n
Figure 3.
Block diagram of LPCC processor.
\n
LPCC have low vulnerability to noise [30]. LPCC features yield lower error rate as compared to LPC features [31]. Cepstral coefficients of higher order are mathematically limited, resulting in an extremely extensive array of variances when moving from the cepstral coefficients of lower order to cepstral coefficients of higher order [34]. Similarly, LPCC estimates are notorious for having great sensitivity to quantization noise [35]. Cepstral analysis on high-pitch speech signal gives small source-filter separability in the quefrency domain [29]. Cepstral coefficients of lower order are sensitive to the spectral slope, while the cepstral coefficients of higher order are sensitive to noise [15].
\n
\n
\n
\n
5. Line spectral frequencies (LSF)
\n
Individual lines of the Line Spectral Pairs (LSP) are known as line spectral frequencies (LSF). LSF defines the two resonance situations taking place in the inter-connected tube model of the human vocal tract. The model takes into consideration the nasal cavity and the mouth shape, which gives the basis for the fundamental physiological importance of the linear prediction illustration. The two resonance situations define the vocal tract as either being completely open or completely closed at the glottis [36]. The two situations begets two groups of resonant frequencies, with the number of resonances in each group being deduced from the quantity of linked tubes. The resonances of each situation are the odd and even line spectra correspondingly, and are interwoven into a singularly rising group of LSF [36].
\n
The LSF representation was proposed by Itakura [37, 38] as a substitute to the linear prediction parametric illustration. In the area of speech coding, it has been realized that this illustration has an improved quantization features than the other linear prediction parametric illustrations (LAR and RC). The LSF illustration has the capacity to reduce the bit-rate by 25–30% for transmitting the linear prediction information without distorting the quality of synthesized speech [38, 39, 40]. Apart from quantization, LSF illustration of the predictor are also suitable for interpolation. Theoretically, this can be inspired by the point that the sensitivity matrix linking the LSF-domain squared quantization error to the perceptually relevant log spectrum is diagonal [41, 42].
\n
\n
5.1. Algorithm description, strength and weaknesses
\n
LP is established on the point that a speech signal can be defined by Eq. (3). Recall
where k is the time index and p is the order of the linear prediction, \n\n\ns\n̂\n\n\nn\n\n\n is the predictor signal and \n\n\na\nk\n\n\n is the LPC coefficients.
\n
The \n\n\na\nk\n\n\n coefficients are determined in order to reduce the prediction error by method of autocorrelation or covariance. Eq. (3) can be modified in the frequency domain with the z-transform. As such, a small part of the speech signal is anticipated to be given as an output to the all-pole filter \n\nH\n\nz\n\n\n. The new equation is
where \n\nH\n\nz\n\n\n is the all-pole filter and \n\nA\n\nz\n\n\n is the LPC analysis filter.
\n
In order to compute the LSF coefficients, an inverse polynomial filter is split into two polynomials \n\nP\n\nz\n\n\n and \n\nQ\n\nz\n\n\n [36, 38, 40, 41]:
The block diagram of the LSF processor is as seen in Figure 4. The most prominent application of LSF is in the area of speech compression, with extension into the speaker recognition and speech recognition. This technique has also found restricted use in other fields. LSF have been investigated for use in musical instrument recognition and coding. LSF have also been applied to animal noise identification, recognizing individual instruments and financial market analysis. The advantages of LSF include their ability to localize spectral sensitivities, the fact that they characterize bandwidths and resonance locations and lays emphasis on the important aspect of spectral peak location. In most instances, the LSF representation provides a near-minimal data set for subsequent classification [36].
\n
Figure 4.
Block diagram of LSF processor.
\n
Since LSF represents spectral shape information at a lower data rate than raw input samples, it is reasonable that a careful use of processing and analysis methods in the LSP domain could lead to a complexity reduction against alternative techniques operating on the raw input data itself. LSF play an important role in the transmission of vocal tract information from speech coder to decoder with their widespread use being a result of their excellent quantization properties. The generation of LSP parameters can be accomplished using several methods, ranging in complexity. The major problem revolves around finding the roots of the P and Q polynomials defined in Eqs. (8) and (9). This can be obtained through standard root solving methods, or more obscure methods and it is often performed in the cosine domain [36].
\n
\n
\n
\n
6. Discrete wavelet transform (dwt)
\n
Wavelet Transform (WT) theory is centered around signal analysis using varying scales in the time and frequency domains [45]. With the support of theoretical physicist Alex Grossmann, Jean Morlet introduced wavelet transform which permits high-frequency events identification with an enhanced temporal resolution [45, 46, 47]. A wavelet is a waveform of effectively limited duration that has an average value of zero. Many wavelets also display orthogonality, an ideal feature of compact signal representation [46]. WT is a signal processing technique that can be used to represent real-life non-stationary signals with high efficiency [33, 46]. It has the ability to mine information from the transient signals concurrently in both time and frequency domains [33, 45, 48].
\n
Continuous wavelet transform (CWT) is used to split a continuous-time function into wavelets. However, there is redundancy of information and huge computational efforts is required to calculate all likely scales and translations of CWT, thereby restricting its use [45]. Discrete wavelet transform (DWT) is an extension of the WT that enhances the flexibility to the decomposition process [48]. It was introduced as a highly flexible and efficient method for sub band breakdown of signals [46, 49]. In earlier applications, linear discretization was used for discretizing CWT. Daubechies and others have developed an orthogonal DWT specially designed for analyzing a finite set of observations over the set of scales (dyadic discretization) [47].
\n
\n
6.1. Algorithm description, strength and weaknesses
\n
Wavelet transform decomposes a signal into a group of basic functions called wavelets. Wavelets are obtained from a single prototype wavelet called mother wavelet by dilations and shifting. The main characteristic of the WT is that it uses a variable window to scan the frequency spectrum, increasing the temporal resolution of the analysis [45, 46, 50].
\n
WT decomposes signals over translated and dilated mother wavelets. Mother wavelet is a time function with finite energy and fast decay. The different versions of the single wavelet are orthogonal to each other. The continuous wavelet transform (CWT) is given by [33, 45, 50]:
where \n\nψ\n\nt\n\n\n is the mother wavelet, a and b are continuous parameters.
\n
The WT coefficient is an expansion and a particular shift represents how well the original signal corresponds to the translated and dilated mother wavelet. Thus, the coefficient group of CWT(a, b) associated with a particular signal is the wavelet representation of the original signal in relation to the mother wavelet [45]. Since CWT contains high redundancy, analyzing the signal using a small number of scales with varying number of translations at each scale, i.e. discretizing scale and translation parameters as \n\na\n=\n\n2\nj\n\n\nand\n\nb\n=\n\n2\nj\n\nk\n\n gives DWT. DWT theory requires two sets of related functions called scaling function and wavelet function given by [33]:
where \n\nϕ\n\nt\n\n\n is the scaling function, \n\nψ\n\nt\n\n\n is the wavelet function, h[n] is the an impulse response of a low-pass filter, and g[n] is an impulse response of a high-pass filter.
\n
There are several ways to discretize a CWT. The DWT of the continuous signal can also be given by [45]:
where g(*) is the mother wavelet and x[n] is the discretized signal. The mother wavelet may be dilated and translated discretely by selecting the scaling parameter \n\na\n=\n\na\n0\nm\n\n\n and translation parameter \n\nb\n=\nn\n\nb\n0\n\n\na\n0\nm\n\n\n (with constants taken as \n\n\na\n0\n\n>\n1\n\n, \n\n\nb\n0\n\n>\n1\n\n, while m and n are assigned a set of positive integers).
\n
The scaling and wavelet functions can be implemented effectively using a pair of filters, h[n] and g[n], called quadrature mirror filters that confirm with the property \n\ng\n\nn\n\n=\n\n\n\n−\n1\n\n\n\n1\n−\nn\n\n\nh\n\nn\n\n\n. The input signal is filtered by a low-pass filter and high-pass filter to obtain the approximate components and the detail components respectively. This is summarized in Figure 5. The approximate signal at each stage is further decomposed using the same low-pass and high-pass filters to get the approximate and detail components for the next stage. This type of decomposition is called dyadic decomposition [33].
\n
Figure 5.
Block diagram of DWT.
\n
The DWT parameters contain the information of different frequency scales. This enhances the speech information obtained in the corresponding frequency band [33]. The ability of the DWT to partition the variance of the elements of the input on a scale by scale basis is an added advantage. This partitioning leads to the opinion of the scale-dependent wavelet variance, which in many ways is equivalent to the more familiar frequency-dependent Fourier power spectrum [47]. Classic discrete decomposition schemes, which are dyadic do not fulfill all the requirements for direct use in parameterization. DWT does provide adequate number of frequency bands for effective speech analysis [51]. Since the input signals are of finite length, the wavelet coefficients will have unwantedly large variations at the boundaries because of the discontinuities at the boundaries [50].
\n
\n
\n
\n
7. Perceptual linear prediction (PLP)
\n
Perceptual linear prediction (PLP) technique combines the critical bands, intensity-to-loudness compression and equal loudness pre-emphasis in the extraction of relevant information from speech. It is rooted in the nonlinear bark scale and was initially intended for use in speech recognition tasks by eliminating the speaker dependent features [11]. PLP gives a representation conforming to a smoothed short-term spectrum that has been equalized and compressed similar to the human hearing making it similar to the MFCC. In the PLP approach, several prominent features of hearing are replicated and the consequent auditory like spectrum of speech is approximated by an autoregressive all–pole model [52]. PLP gives minimized resolution at high frequencies that signifies auditory filter bank based approach, yet gives the orthogonal outputs that are similar to the cepstral analysis. It uses linear predictions for spectral smoothing, hence, the name is perceptual linear prediction [28]. PLP is a combination of both spectral analysis and linear prediction analysis.
\n
\n
7.1. Algorithm description, strength and weaknesses
\n
In order to compute the PLP features the speech is windowed (Hamming window), the Fast Fourier Transform (FFT) and the square of the magnitude are computed. This gives the power spectral estimates. A trapezoidal filter is then applied at 1-bark interval to integrate the overlapping critical band filter responses in the power spectrum. This effectively compresses the higher frequencies into a narrow band. The symmetric frequency domain convolution on the bark warped frequency scale then permits low frequencies to mask the high frequencies, concurrently smoothing the spectrum. The spectrum is subsequently pre-emphasized to approximate the uneven sensitivity of human hearing at a variety of frequencies. The spectral amplitude is compressed, this reduces the amplitude variation of the spectral resonances. An Inverse Discrete Fourier Transform (IDCT) is performed to get the autocorrelation coefficients. Spectral smoothing is performed, solving the autoregressive equations. The autoregressive coefficients are converted to cepstral variables [28]. The equation for computing the bark scale frequency is:
where bark(f) is the frequency (bark) and f is the frequency (Hz).
\n
The identification achieved by PLP is better than that of LPC [28], because it is an improvement over the conventional LPC because it effectively suppresses the speaker-dependent information [52]. Also, it has enhanced speaker independent recognition performance and is robust to noise, variations in the channel and microphones [53]. PLP reconstructs the autoregressive noise component accurately [54]. PLP based front end is sensitive to any change in the formant frequency.
\n
Figure 6 shows the PLP processor, showing all the steps to be taken to obtain the PLP coefficients. PLP has low sensitivity to spectral tilt, consistent with the findings that it is relatively insensitive to phonetic judgments of the spectral tilt. Also, PLP analysis is dependent on the result of the overall spectral balance (formant amplitudes). The formant amplitudes are easily affected by factors such as the recording equipment, communication channel and additive noise [52]. Furthermore, the time-frequency resolution and efficient sampling of the short-term representation are addressed in an ad-hoc way [54].
\n
Figure 6.
Block diagram of PLP processor.
\n
Table 1 shows a comparison between the six feature extraction techniques that have been explicitly described above. Even though the selection of a feature extraction algorithm for use in research is individual dependent, however, this table has been able to characterize these techniques based on the main considerations in the selection of any feature extraction algorithm. The considerations include speed of computation, noise resistance and sensitivity to additional noise. The table also serves as a guide when considering the selection between any two or more of the discussed algorithms.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
\n
Type of Filter
\n
Shape of filter
\n
What is modeled
\n
Speed of computation
\n
Type of coefficient
\n
Noise resistance
\n
Sensitivity to quantization/additional noise
\n
Reliability
\n
Frequency captured
\n
\n\n\n
\n
Mel frequency cepstral coefficient (MFCC)
\n
Mel
\n
Triangular
\n
Human Auditory System
\n
High
\n
Cepstral
\n
Medium
\n
Medium
\n
High
\n
Low
\n
\n
\n
Linear prediction coefficient (LPC)
\n
Linear Prediction
\n
Linear
\n
Human Vocal Tract
\n
High
\n
Autocorrelation Coefficient
\n
High
\n
High
\n
High
\n
Low
\n
\n
\n
Linear prediction cepstral coefficient (LPCC)
\n
Linear Prediction
\n
Linear
\n
Human Vocal Tract
\n
Medium
\n
Cepstral
\n
High
\n
High
\n
Medium
\n
Low & Medium
\n
\n
\n
Line spectral frequencies (LSF)
\n
Linear Prediction
\n
Linear
\n
Human Vocal Tract
\n
Medium
\n
Spectral
\n
High
\n
High
\n
Medium
\n
Low & Medium
\n
\n
\n
Discrete wavelet transform (DWT)
\n
Lowpass & highpass
\n
—
\n
—
\n
High
\n
Wavelets
\n
Medium
\n
Medium
\n
Medium
\n
Low & High
\n
\n
\n
Perceptual linear prediction (PLP)
\n
Bark
\n
Trapezoidal
\n
Human Auditory System
\n
Medium
\n
Cepstral & Autocorrelation
\n
Medium
\n
Medium
\n
Medium
\n
Low & Medium
\n
\n\n
Table 1.
Comparison between the feature extraction techniques.
\n
\n
\n
\n
8. Conclusion
\n
MFCC, LPC, LPCC, LSF, PLP and DWT are some of the feature extraction techniques used for extracting relevant information form speech signals for the purpose speech recognition and identification. These techniques have stood the test of time and have been widely used in speech recognition systems for several purposes. Speech signal is a slow time varying signal, quasi-stationary, when observed over an adequately short period of time between 5 and 100 msec, its behavior is relatively stationary. As a result of this, short time spectral analysis which includes MFCC, LPCC and PLP are commonly used for the extraction of important information from speech signals. Noise is a serious challenge encountered in the process of feature extraction, as well as speaker recognition as a whole. Subsequently, researchers have made several modifications to the above discussed techniques to make them less susceptible to noise, more robust and consume less time. These methods have also been used in the recognition of sounds. The extracted information will be the input to the classifier for identification purposes. The above discussed feature extraction approaches can be implemented using MATLAB.
\n
\n\n',keywords:"human speech, speech features, mel frequency cepstral coefficients (MFCC), linear prediction coefficients (LPC), linear prediction cepstral coefficients (LPCC), line spectral frequencies (LSF), discrete wavelet transform (DWT), perceptual linear prediction (PLP)",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/63970.pdf",chapterXML:"https://mts.intechopen.com/source/xml/63970.xml",downloadPdfUrl:"/chapter/pdf-download/63970",previewPdfUrl:"/chapter/pdf-preview/63970",totalDownloads:1989,totalViews:723,totalCrossrefCites:8,totalDimensionsCites:9,hasAltmetrics:0,dateSubmitted:"October 4th 2017",dateReviewed:"July 20th 2018",datePrePublished:null,datePublished:"December 12th 2018",dateFinished:null,readingETA:"0",abstract:"Speech is a complex naturally acquired human motor ability. It is characterized in adults with the production of about 14 different sounds per second via the harmonized actions of roughly 100 muscles. Speaker recognition is the capability of a software or hardware to receive speech signal, identify the speaker present in the speech signal and recognize the speaker afterwards. Feature extraction is accomplished by changing the speech waveform to a form of parametric representation at a relatively minimized data rate for subsequent processing and analysis. Therefore, acceptable classification is derived from excellent and quality features. Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), Line Spectral Frequencies (LSF), Discrete Wavelet Transform (DWT) and Perceptual Linear Prediction (PLP) are the speech feature extraction techniques that were discussed in these chapter. These methods have been tested in a wide variety of applications, giving them high level of reliability and acceptability. Researchers have made several modifications to the above discussed techniques to make them less susceptible to noise, more robust and consume less time. In conclusion, none of the methods is superior to the other, the area of application would determine which method to select.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/63970",risUrl:"/chapter/ris/63970",book:{slug:"from-natural-to-artificial-intelligence-algorithms-and-applications"},signatures:"Sabur Ajibola Alim and Nahrul Khair Alang Rashid",authors:[{id:"224685",title:"Dr.",name:"Sabur",middleName:"Ajibola",surname:"Alim",fullName:"Sabur Alim",slug:"sabur-alim",email:"moaj1st@yahoo.com",position:null,institution:null},{id:"225647",title:"Prof.",name:"Nahrul Khair",middleName:null,surname:"Alang Rashid",fullName:"Nahrul Khair Alang Rashid",slug:"nahrul-khair-alang-rashid",email:"nahrulk@gmail.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Mel frequency cepstral coefficients (MFCC)",level:"1"},{id:"sec_2_2",title:"2.1. Algorithm description, strength and weaknesses",level:"2"},{id:"sec_4",title:"3. Linear prediction coefficients (LPC)",level:"1"},{id:"sec_4_2",title:"3.1. Algorithm description, strength and weaknesses",level:"2"},{id:"sec_6",title:"4. Linear prediction cepstral coefficients (LPCC)",level:"1"},{id:"sec_6_2",title:"4.1. Algorithm description, strength and weaknesses",level:"2"},{id:"sec_8",title:"5. Line spectral frequencies (LSF)",level:"1"},{id:"sec_8_2",title:"5.1. Algorithm description, strength and weaknesses",level:"2"},{id:"sec_10",title:"6. Discrete wavelet transform (dwt)",level:"1"},{id:"sec_10_2",title:"6.1. Algorithm description, strength and weaknesses",level:"2"},{id:"sec_12",title:"7. Perceptual linear prediction (PLP)",level:"1"},{id:"sec_12_2",title:"7.1. Algorithm description, strength and weaknesses",level:"2"},{id:"sec_14",title:"8. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Hariharan M, Vijean V, Fook CY, Yaacob S. Speech stuttering assessment using sample entropy and Least Square Support vector machine. In: 8th International Colloquium on Signal Processing and its Applications (CSPA). 2012. pp. 240-245\n'},{id:"B2",body:'Manjula GN, Kumar MS. Stuttered speech recognition for robotic control. International Journal of Engineering and Innovative Technology (IJEIT). 2014;3(12):174-177\n'},{id:"B3",body:'Duffy JR. Motor speech disorders: Clues to neurologic diagnosis. In: Parkinson’s Disease and Movement Disorders. Totowa, NJ: Humana Press; 2000. pp. 35-53\n'},{id:"B4",body:'Kurzekar PK, Deshmukh RR, Waghmare VB, Shrishrimal PP. A comparative study of feature extraction techniques for speech recognition system. International Journal of Innovative Research in Science, Engineering and Technology. 2014;3(12):18006-18016\n'},{id:"B5",body:'Ahmad AM, Ismail S, Samaon DF. Recurrent neural network with backpropagation through time for speech recognition. In: IEEE International Symposium on Communications and Information Technology (ISCIT 2004). Vol. 1. Sapporo, Japan: IEEE; 2004. pp. 98-102\n'},{id:"B6",body:'Shaneh M, Taheri A. Voice command recognition system based on MFCC and VQ algorithms. World academy of science. Engineering and Technology. 2009;57:534-538\n'},{id:"B7",body:'Mosa GS, Ali AA. Arabic phoneme recognition using hierarchical neural fuzzy petri net and LPC feature extraction. Signal Processing: An International Journal (SPIJ). 2009;3(5):161\n'},{id:"B8",body:'Yousefian N, Analoui M. Using radial basis probabilistic neural network for speech recognition. In: Proceeding of 3rd International Conference on Information and Knowledge (IKT07), Mashhad, Iran. 2007\n'},{id:"B9",body:'Cornaz C, Hunkeler U, Velisavljevic V. An Automatic Speaker Recognition System. Switzerland: Lausanne; 2003. Retrieved from: http://read.pudn.com/downloads60/sourcecode/multimedia/audio/209082/asr_project.pdf\n\n'},{id:"B10",body:'Shah SAA, ul Asar A, Shaukat SF. Neural network solution for secure interactive voice response. World Applied Sciences Journal. 2009;6(9):1264-1269\n'},{id:"B11",body:'Ravikumar KM, Rajagopal R, Nagaraj HC. An approach for objective assessment of stuttered speech using MFCC features. ICGST International Journal on Digital Signal Processing, DSP. 2009;9(1):19-24\n'},{id:"B12",body:'Kumar PP, Vardhan KSN, Krishna KSR. Performance evaluation of MLP for speech recognition in noisy environments using MFCC & wavelets. International Journal of Computer Science & Communication (IJCSC). 2010;1(2):41-45\n'},{id:"B13",body:'Kumar R, Ranjan R, Singh SK, Kala R, Shukla A, Tiwari R. Multilingual speaker recognition using neural network. In: Proceedings of the Frontiers of Research on Speech and Music, FRSM. 2009. pp. 1-8\n'},{id:"B14",body:'Narang S, Gupta MD. Speech feature extraction techniques: A review. International Journal of Computer Science and Mobile Computing. 2015;4(3):107-114\n'},{id:"B15",body:'Al-Alaoui MA, Al-Kanj L, Azar J, Yaacoub E. Speech recognition using artificial neural networks and hidden Markov models. IEEE Multidisciplinary Engineering Education Magazine. 2008;3(3):77-86\n'},{id:"B16",body:'Al-Sarayreh KT, Al-Qutaish RE, Al-Kasasbeh BM. Using the sound recognition techniques to reduce the electricity consumption in highways. Journal of American Science. 2009;5(2):1-12\n'},{id:"B17",body:'Gill AS. A review on feature extraction techniques for speech processing. International Journal Of Engineering and Computer Science. 2016;5(10):18551-18556\n'},{id:"B18",body:'Othman AM, Riadh MH. Speech recognition using scaly neural networks. World academy of science. Engineering and Technology. 2008;38:253-258\n'},{id:"B19",body:'Chakroborty S, Roy A, Saha G. Fusion of a complementary feature set with MFCC for improved closed set text-independent speaker identification. In: IEEE International Conference on Industrial Technology, 2006. ICIT 2006. pp. 387-390\n'},{id:"B20",body:'de Lara JRC. A method of automatic speaker recognition using cepstral features and vectorial quantization. In: Iberoamerican Congress on Pattern Recognition. Berlin, Heidelberg: Springer; 2005. pp. 146-153\n'},{id:"B21",body:'Ravikumar KM, Reddy BA, Rajagopal R, Nagaraj HC. Automatic detection of syllable repetition in read speech for objective assessment of stuttered Disfluencies. In: Proceedings of World Academy Science, Engineering and Technology. 2008. pp. 270-273\n'},{id:"B22",body:'Hasan MR, Jamil M, Rabbani G, Rahman MGRMS. Speaker Identification Using Mel Frequency cepstral coefficients. In: 3rd International Conference on Electrical & Computer Engineering, 2004. ICECE 2004. pp. 28-30\n'},{id:"B23",body:'Chu S, Narayanan S, Kuo CC. Environmental sound recognition using MP-based features. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE; 2008. pp. 1-4\n'},{id:"B24",body:'Rao TB, Reddy PPVGD, Prasad A. Recognition and a panoramic view of Raaga emotions of singers-application Gaussian mixture model. International Journal of Research and Reviews in Computer Science (IJRRCS). 2011;2(1):201-204\n'},{id:"B25",body:'Agrawal S, Shruti AK, Krishna CR. Prosodic feature based text dependent speaker recognition using machine learning algorithms. International Journal of Engineering Science and Technology. 2010;2(10):5150-5157\n'},{id:"B26",body:'Paulraj MP, Sazali Y, Nazri A, Kumar S. A speech recognition system for Malaysian English pronunciation using neural network. In: Proceedings of the International Conference on Man-Machine Systems (ICoMMS). 2009\n'},{id:"B27",body:'Tan CL, Jantan A. Digit recognition using neural networks. Malaysian Journal of Computer Science. 2004;17(2):40-54\n'},{id:"B28",body:'Kumar P, Chandra M. Speaker identification using Gaussian mixture models. MIT International Journal of Electronics and Communication Engineering. 2011;1(1):27-30\n'},{id:"B29",body:'Wang TT, Quatieri TF. High-pitch formant estimation by exploiting temporal change of pitch. IEEE Transactions on Audio, Speech, and Language Processing. 2010;18(1):171-186\n'},{id:"B30",body:'El Choubassi MM, El Khoury HE, Alagha CEJ, Skaf JA, Al-Alaoui MA. Arabic speech recognition using recurrent neural networks. In: Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology (IEEE Cat. No.03EX795). Ieee; 2003. pp. 543-547. DOI: 10.1109/ISSPIT.2003.1341178\n'},{id:"B31",body:'Wu QZ, Jou IC, Lee SY. On-line signature verification using LPC cepstrum and neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 1997;27(1):148-153\n'},{id:"B32",body:'Holambe R, Deshpande M. Advances in Non-Linear Modeling for Speech Processing. Berlin, Heidelberg: Springer Science & Business Media; 2012\n'},{id:"B33",body:'Nehe NS, Holambe RS. DWT and LPC based feature extraction methods for isolated word recognition. EURASIP Journal on Audio, Speech, and Music Processing. 2012;2012(1):7\n'},{id:"B34",body:'Young S, Evermann G, Gales M, Hain T, Kershaw D, Liu X, et al. The HTK Book, Version 3.4. Cambridge, United Kingdom: Cambridge University; 2006\n'},{id:"B35",body:'Ismail S, Ahmad A. Recurrent neural network with backpropagation through time algorithm for arabic recognition. In: Proceedings of the 18th European Simulation Multiconference (ESM). Magdeburg, Germany; 2004. pp. 13-16\n'},{id:"B36",body:'McLoughlin IV. Line spectral pairs. Signal Processing. 2008;88(3):448-467\n'},{id:"B37",body:'Itakura F. Line spectrum representation of linear predictor coefficients of speech signals. The Journal of the Acoustical Society of America. 1975;57(S1):S35-S35\n'},{id:"B38",body:'Silva DF, de Souza VM, Batista GE, Giusti R. Spoken digit recognition in portuguese using line spectral frequencies. Ibero-American Conference on Artificial Intelligence. Vol. 7637. Berlin, Heidelberg: Springer; 2012. pp. 241-250\n'},{id:"B39",body:'Kabal P, Ramachandran RP. The computation of line spectral frequencies using Chebyshev polynomials. IEEE Transactions on Acoustics, Speech and Signal Processing. 1986;34(6):1419-1426\n'},{id:"B40",body:'Paliwal KK. On the use of line spectral frequency parameters for speech recognition. Digital Signal Processing. 1992;2(2):80-87\n'},{id:"B41",body:'Alang Rashid NK, Alim SA, Hashim NNWNH, Sediono W. Receiver operating characteristics measure for the recognition of stuttering Dysfluencies using line spectral frequencies. IIUM Engineering Journal. 2017;18(1):193-200\n'},{id:"B42",body:'Kleijn WB, Bäckström T, Alku P. On line spectral frequencies. IEEE Signal Processing Letters. 2003;10(3):75-77\n'},{id:"B43",body:'Bäckström T, Pedersen CF, Fischer J, Pietrzyk G. Finding line spectral frequencies using the fast Fourier transform. In: 2015 IEEE International Conference on in Acoustics, Speech and Signal Processing (ICASSP). 2015. pp. 5122-5126\n'},{id:"B44",body:'Nematollahi MA, Vorakulpipat C, Gamboa Rosales H. Semifragile speech watermarking based on least significant bit replacement of line spectral frequencies. Mathematical Problems in Engineering. 2017. 9 p\n'},{id:"B45",body:'Oliveira MO, Bretas AS. Application of discrete wavelet transform for differential protection of power transformers. In: IEEE PowerTech. Bucharest: IEEE; 2009. pp. 1-8\n'},{id:"B46",body:'Gupta D, Choubey S. Discrete wavelet transform for image processing. International Journal of Emerging Technology and Advanced Engineering. 2015;4(3):598-602\n'},{id:"B47",body:'Lindsay RW, Percival DB, Rothrock DA. The discrete wavelet transform and the scale analysis of the surface properties of sea ice. IEEE Transactions on Geoscience and Remote Sensing. 1996;34(3):771-787\n'},{id:"B48",body:'Turner C, Joseph A. A wavelet packet and mel-frequency cepstral coefficients-based feature extraction method for speaker identification. In: Procedia Computer Science. 2015. pp. 416-421\n'},{id:"B49",body:'Reig-Bolaño R, Marti-Puig P, Solé-Casals J, Zaiats V, Parisi V. Coding of biosignals using the discrete wavelet decomposition. In: International Conference on Nonlinear Speech Processing. Berlin Heidelberg: Springer; 2009. pp. 144-151\n'},{id:"B50",body:'Tufekci Z, Gowdy JN. Feature extraction using discrete wavelet transform for speech recognition. In: IEEE Southeastcon 2000. 2000. pp. 116-123\n'},{id:"B51",body:'Gałka J, Ziółko M. Wavelet speech feature extraction using mean best basis algorithm. In: International Conference on Nonlinear Speech Processing Berlin. Heidelberg: Springer; 2009. pp. 128-135\n'},{id:"B52",body:'Hermansky H. Perceptual linear predictive (PLP) analysis of speech. The Journal of the Acoustical Society of America. 1990;87(4):1738-1752\n'},{id:"B53",body:'Picone J. Fundamentals of Speech Recognition: Spectral Transformations. 2011. Retrieved from: http://www.isip.piconepress.com/publications/courses/msstate/ece_8463/lectures/current/lecture_17/lecture_17.pdf\n\n'},{id:"B54",body:'Thomas S, Ganapathy S, Hermansky H. Spectro-temporal features for automatic speech recognition using linear prediction in spectral domain. In: Proceedings of the 16th European Signal Processing Conference (EUSIPCO 2008), Lausanne, Switzerland. 2008\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Sabur Ajibola Alim",address:"moaj1st@yahoo.com",affiliation:'
'}],corrections:null},book:{id:"6554",title:"From Natural to Artificial Intelligence",subtitle:"Algorithms and Applications",fullTitle:"From Natural to Artificial Intelligence - Algorithms and Applications",slug:"from-natural-to-artificial-intelligence-algorithms-and-applications",publishedDate:"December 12th 2018",bookSignature:"Ricardo Lopez-Ruiz",coverURL:"https://cdn.intechopen.com/books/images_new/6554.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"9849",title:"Prof.",name:"Ricardo",middleName:null,surname:"Lopez-Ruiz",slug:"ricardo-lopez-ruiz",fullName:"Ricardo Lopez-Ruiz"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},chapters:[{id:"63970",title:"Some Commonly Used Speech Feature Extraction Algorithms",slug:"some-commonly-used-speech-feature-extraction-algorithms",totalDownloads:1989,totalCrossrefCites:8,signatures:"Sabur Ajibola Alim and Nahrul Khair Alang Rashid",authors:[{id:"224685",title:"Dr.",name:"Sabur",middleName:"Ajibola",surname:"Alim",fullName:"Sabur Alim",slug:"sabur-alim"},{id:"225647",title:"Prof.",name:"Nahrul Khair",middleName:null,surname:"Alang Rashid",fullName:"Nahrul Khair Alang Rashid",slug:"nahrul-khair-alang-rashid"}]},{id:"63017",title:"Convolutional Neural Networks for Raw Speech Recognition",slug:"convolutional-neural-networks-for-raw-speech-recognition",totalDownloads:1793,totalCrossrefCites:5,signatures:"Vishal Passricha and Rajesh Kumar Aggarwal",authors:[{id:"256038",title:"Prof.",name:"Rajesh",middleName:null,surname:"Aggarwal",fullName:"Rajesh Aggarwal",slug:"rajesh-aggarwal"},{id:"256039",title:"Mr.",name:"Vishal",middleName:null,surname:"Passricha",fullName:"Vishal Passricha",slug:"vishal-passricha"}]},{id:"61777",title:"Evaluation between Virtual Acoustic Model and Real Acoustic Scenarios for Urban Representation",slug:"evaluation-between-virtual-acoustic-model-and-real-acoustic-scenarios-for-urban-representation",totalDownloads:555,totalCrossrefCites:1,signatures:"Josep Llorca, Héctor Zapata, Jesús Alba, Ernest Redondo and David Fonseca",authors:[{id:"224125",title:"Dr.",name:"Ernest",middleName:null,surname:"Redondo",fullName:"Ernest Redondo",slug:"ernest-redondo"},{id:"225606",title:"Ph.D. Student",name:"Josep",middleName:null,surname:"Llorca",fullName:"Josep Llorca",slug:"josep-llorca"},{id:"241352",title:"MSc.",name:"Héctor",middleName:null,surname:"Zapata",fullName:"Héctor Zapata",slug:"hector-zapata"},{id:"241353",title:"Dr.",name:"Jesús",middleName:null,surname:"Alba",fullName:"Jesús Alba",slug:"jesus-alba"},{id:"241354",title:"Dr.",name:"David",middleName:null,surname:"Fonseca",fullName:"David Fonseca",slug:"david-fonseca"}]},{id:"64164",title:"Formative E-Assessment of Schema Acquisition in the Human Lexicon as a Tool in Adaptive Online Instruction",slug:"formative-e-assessment-of-schema-acquisition-in-the-human-lexicon-as-a-tool-in-adaptive-online-instr",totalDownloads:613,totalCrossrefCites:0,signatures:"Guadalupe Elizabeth Morales-Martinez, Yanko Norberto Mezquita-Hoyos, Claudia Jaquelina Gonzalez-Trujillo, Ernesto Octavio Lopez-Ramirez and Jocelyn Pamela Garcia-Duran",authors:[{id:"251445",title:"Dr.",name:"Guadalupe",middleName:"Elizabeth",surname:"Morales-Martinez",fullName:"Guadalupe Morales-Martinez",slug:"guadalupe-morales-martinez"},{id:"251446",title:"Dr.",name:"Ernesto",middleName:null,surname:"Lopez-Ramirez",fullName:"Ernesto Lopez-Ramirez",slug:"ernesto-lopez-ramirez"},{id:"251448",title:"BSc.",name:"Jocelyn",middleName:null,surname:"Garcia-Duran",fullName:"Jocelyn Garcia-Duran",slug:"jocelyn-garcia-duran"},{id:"259836",title:"Dr.",name:"Claudia Jaquelina",middleName:null,surname:"Gonzalez-Trujillo",fullName:"Claudia Jaquelina Gonzalez-Trujillo",slug:"claudia-jaquelina-gonzalez-trujillo"},{id:"259837",title:"Dr.",name:"Yanko",middleName:null,surname:"Mezquita-Hoyos",fullName:"Yanko Mezquita-Hoyos",slug:"yanko-mezquita-hoyos"}]},{id:"61281",title:"Local Patterns for Face Recognition",slug:"local-patterns-for-face-recognition",totalDownloads:465,totalCrossrefCites:0,signatures:"Chih-Wei Lin",authors:[{id:"228878",title:"Ph.D.",name:"Chih-Wei",middleName:null,surname:"Lin",fullName:"Chih-Wei Lin",slug:"chih-wei-lin"}]},{id:"60862",title:"Face Recognition Based on Texture Descriptors",slug:"face-recognition-based-on-texture-descriptors",totalDownloads:494,totalCrossrefCites:0,signatures:"Jesus Olivares-Mercado, Karina Toscano-Medina, Gabriel Sanchez-Perez, Mariko Nakano Miyatake, Hector Perez-Meana and Luis Carlos Castro-Madrid",authors:[{id:"30416",title:"Prof.",name:"Hector",middleName:null,surname:"Perez Meana",fullName:"Hector Perez Meana",slug:"hector-perez-meana"},{id:"43113",title:"Dr.",name:"Jesus",middleName:null,surname:"Olivares-Mercado",fullName:"Jesus Olivares-Mercado",slug:"jesus-olivares-mercado"},{id:"85308",title:"Dr.",name:"Karina",middleName:null,surname:"Toscano-Medina",fullName:"Karina Toscano-Medina",slug:"karina-toscano-medina"},{id:"109424",title:"Dr.",name:"Gabriel",middleName:null,surname:"Sanchez",fullName:"Gabriel Sanchez",slug:"gabriel-sanchez"},{id:"113946",title:"Dr.",name:"Mariko",middleName:null,surname:"Nakano Miyatake",fullName:"Mariko Nakano Miyatake",slug:"mariko-nakano-miyatake"},{id:"240226",title:"MSc.",name:"Luis Carlos",middleName:null,surname:"Castro Madrid",fullName:"Luis Carlos Castro Madrid",slug:"luis-carlos-castro-madrid"}]},{id:"62992",title:"Learning Algorithms for Fuzzy Inference Systems Using Vector Quantization",slug:"learning-algorithms-for-fuzzy-inference-systems-using-vector-quantization",totalDownloads:517,totalCrossrefCites:0,signatures:"Hirofumi Miyajima, Noritaka Shigei and Hiromi Miyajima",authors:[{id:"259341",title:"Dr.",name:"Hiromi",middleName:null,surname:"Miyajima",fullName:"Hiromi Miyajima",slug:"hiromi-miyajima"},{id:"259372",title:"Dr.",name:"Hirofumi",middleName:null,surname:"Miyajima",fullName:"Hirofumi Miyajima",slug:"hirofumi-miyajima"},{id:"259373",title:"Dr.",name:"Noritaka",middleName:null,surname:"Shigei",fullName:"Noritaka Shigei",slug:"noritaka-shigei"}]},{id:"61402",title:"Query Morphing: A Proximity-Based Approach for Data Exploration",slug:"query-morphing-a-proximity-based-approach-for-data-exploration",totalDownloads:530,totalCrossrefCites:0,signatures:"Jay Patel and Vikram Singh",authors:[{id:"228092",title:"Mr.",name:"Vikram",middleName:null,surname:"Singh",fullName:"Vikram Singh",slug:"vikram-singh"},{id:"228093",title:"Mr.",name:"Jay",middleName:null,surname:"Patel",fullName:"Jay Patel",slug:"jay-patel"}]},{id:"62760",title:"Cellular Automata and Randomization: A Structural Overview",slug:"cellular-automata-and-randomization-a-structural-overview",totalDownloads:514,totalCrossrefCites:0,signatures:"Monica Dascălu",authors:[{id:"249893",title:"Prof.",name:"Monica",middleName:null,surname:"Dascalu",fullName:"Monica Dascalu",slug:"monica-dascalu"}]},{id:"63819",title:"Hard, firm, soft … Etherealware:Computing by Temporal Order of Clocking",slug:"hard-firm-soft-etherealware-br-computing-by-temporal-order-of-clocking",totalDownloads:459,totalCrossrefCites:0,signatures:"Michael Vielhaber",authors:[{id:"191101",title:"Prof.",name:"Michael",middleName:null,surname:"Vielhaber",fullName:"Michael Vielhaber",slug:"michael-vielhaber"}]}]},relatedBooks:[{type:"book",id:"5198",title:"Numerical Simulation",subtitle:"From Brain Imaging to Turbulent Flows",isOpenForSubmission:!1,hash:"6bf6d0e6b25e77e717dd3b6c9d494cf9",slug:"numerical-simulation-from-brain-imaging-to-turbulent-flows",bookSignature:"Ricardo Lopez-Ruiz",coverURL:"https://cdn.intechopen.com/books/images_new/5198.jpg",editedByType:"Edited by",editors:[{id:"9849",title:"Prof.",name:"Ricardo",surname:"Lopez-Ruiz",slug:"ricardo-lopez-ruiz",fullName:"Ricardo Lopez-Ruiz"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"},chapters:[{id:"50911",title:"BOLD fMRI Simulation",slug:"bold-fmri-simulation",signatures:"Zikuan Chen and Vince Calhoun",authors:[{id:"179437",title:"Ph.D.",name:"Zikuan",middleName:null,surname:"Chen",fullName:"Zikuan Chen",slug:"zikuan-chen"}]},{id:"50398",title:"Basics of Multibody Systems: Presented by Practical Simulation Examples of Spine Models",slug:"basics-of-multibody-systems-presented-by-practical-simulation-examples-of-spine-models",signatures:"Bauer Sabine",authors:[{id:"180120",title:"Dr.",name:"Sabine",middleName:null,surname:"Bauer",fullName:"Sabine Bauer",slug:"sabine-bauer"}]},{id:"51596",title:"Simulation of Neural Behavior",slug:"simulation-of-neural-behavior",signatures:"Tatsuo Kitajima, Zonggang Feng and Azran Azhim",authors:[{id:"180149",title:"Prof.",name:"Tatsuo",middleName:null,surname:"Kitajima",fullName:"Tatsuo Kitajima",slug:"tatsuo-kitajima"},{id:"184933",title:"Prof.",name:"Zhonggang",middleName:null,surname:"Feng",fullName:"Zhonggang Feng",slug:"zhonggang-feng"},{id:"184934",title:"Prof.",name:"Azran",middleName:null,surname:"Azhim",fullName:"Azran Azhim",slug:"azran-azhim"}]},{id:"50522",title:"Numerical Simulations of Dynamics Behaviour of the Action Potential of the Human Heart's Conduction System",slug:"numerical-simulations-of-dynamics-behaviour-of-the-action-potential-of-the-human-heart-s-conduction-",signatures:"Beata Jackowska-Zduniak",authors:[{id:"180003",title:"Dr.",name:"Beata",middleName:null,surname:"Jackowska-Zduniak",fullName:"Beata Jackowska-Zduniak",slug:"beata-jackowska-zduniak"}]},{id:"51085",title:"Numerical Simulation Using Artificial Neural Network on Fractional Differential Equations",slug:"numerical-simulation-using-artificial-neural-network-on-fractional-differential-equations",signatures:"Najeeb Alam Khan, Amber Shaikh, Faqiha Sultan and Asmat Ara",authors:[{id:"180327",title:"Dr.",name:"Najeeb",middleName:null,surname:"Khan",fullName:"Najeeb Khan",slug:"najeeb-khan"},{id:"184888",title:"Dr.",name:"Amber",middleName:null,surname:"Shaikh",fullName:"Amber Shaikh",slug:"amber-shaikh"},{id:"184889",title:"Dr.",name:"Sidra",middleName:null,surname:"Khan",fullName:"Sidra Khan",slug:"sidra-khan"},{id:"184890",title:"Dr.",name:"Faqiha",middleName:null,surname:"Sultan",fullName:"Faqiha Sultan",slug:"faqiha-sultan"}]},{id:"51603",title:"Numerical Simulations of Some Real-Life Problems Governed by ODEs",slug:"numerical-simulations-of-some-real-life-problems-governed-by-odes",signatures:"N. H. Sweilam and T. A. Assiri",authors:[{id:"180285",title:"Prof.",name:"Nasser",middleName:null,surname:"Sweilam",fullName:"Nasser Sweilam",slug:"nasser-sweilam"}]},{id:"50913",title:"A Multi-Domain Spectral Collocation Approach for Solving Lane-Emden Type Equations",slug:"a-multi-domain-spectral-collocation-approach-for-solving-lane-emden-type-equations",signatures:"Motsa Sandile Sydney, Magagula Vusi Mpendulo, Goqo Sicelo\nPraisegod, Oyelakin Ibukun Sarah and Sibanda Precious",authors:[{id:"18031",title:"Prof.",name:"Sandile",middleName:null,surname:"Motsa",fullName:"Sandile Motsa",slug:"sandile-motsa"},{id:"41622",title:"Prof.",name:"Precious",middleName:null,surname:"Sibanda",fullName:"Precious Sibanda",slug:"precious-sibanda"},{id:"180865",title:"Dr.",name:"Vusi",middleName:"Mpendulo",surname:"Magagula",fullName:"Vusi Magagula",slug:"vusi-magagula"},{id:"180867",title:"Mr.",name:"Sicelo",middleName:null,surname:"Goqo",fullName:"Sicelo Goqo",slug:"sicelo-goqo"},{id:"180868",title:"Ms.",name:"Ibukun",middleName:null,surname:"Oyelakin",fullName:"Ibukun Oyelakin",slug:"ibukun-oyelakin"}]},{id:"51721",title:"Numerical Solution of System of Fractional Differential Equations in Imprecise Environment",slug:"numerical-solution-of-system-of-fractional-differential-equations-in-imprecise-environment",signatures:"Najeeb Alam Khan, Oyoon Abdul Razzaq, Asmat Ara and Fatima\nRiaz",authors:[{id:"180327",title:"Dr.",name:"Najeeb",middleName:null,surname:"Khan",fullName:"Najeeb Khan",slug:"najeeb-khan"},{id:"181244",title:"Ph.D. Student",name:"Fatima",middleName:null,surname:"Riaz",fullName:"Fatima Riaz",slug:"fatima-riaz"},{id:"181248",title:"Dr.",name:"Oyoon",middleName:null,surname:"Razzaq",fullName:"Oyoon Razzaq",slug:"oyoon-razzaq"},{id:"184887",title:"Dr.",name:"Asmat",middleName:null,surname:"Ara",fullName:"Asmat Ara",slug:"asmat-ara"}]},{id:"51210",title:"Analysis of Heat Transfer in an Experimental Heat Exchanger Using Numerical Simulation",slug:"analysis-of-heat-transfer-in-an-experimental-heat-exchanger-using-numerical-simulation",signatures:"Laura L. Castro, Alfredo Aranda and Gustavo Urquiza",authors:[{id:"108593",title:"Dr.",name:"Gustavo",middleName:null,surname:"Urquiza",fullName:"Gustavo Urquiza",slug:"gustavo-urquiza"},{id:"179471",title:"Dr.",name:"Laura",middleName:null,surname:"Castro Gómez",fullName:"Laura Castro Gómez",slug:"laura-castro-gomez"}]},{id:"51534",title:"Solving Inverse Heat Transfer Problems When Using CFD Modeling",slug:"solving-inverse-heat-transfer-problems-when-using-cfd-modeling",signatures:"Paweł Ludowski, Dawid Taler and Jan Taler",authors:[{id:"15203",title:"Dr.",name:"Dawid",middleName:null,surname:"Taler",fullName:"Dawid Taler",slug:"dawid-taler"},{id:"43955",title:"Prof.",name:"Jan",middleName:"Marian",surname:"Taler",fullName:"Jan Taler",slug:"jan-taler"},{id:"179938",title:"Ph.D.",name:"Paweł",middleName:null,surname:"Ludowski",fullName:"Paweł Ludowski",slug:"pawel-ludowski"}]},{id:"51422",title:"A Numerical Procedure for 2D Fluid Flow Simulation in Unstructured Meshes",slug:"a-numerical-procedure-for-2d-fluid-flow-simulation-in-unstructured-meshes",signatures:"António M. G. Lopes",authors:[{id:"180158",title:"Ph.D.",name:"Antonio",middleName:null,surname:"Gameiro Lopes",fullName:"Antonio Gameiro Lopes",slug:"antonio-gameiro-lopes"}]},{id:"51081",title:"On a New Numerical Approach on Micropolar Fluid, Heat and Mass Transfer Over an Unsteady Stretching Sheet Through Porous Media in the Presence of a Heat Source/Sink and Chemical Reaction",slug:"on-a-new-numerical-approach-on-micropolar-fluid-heat-and-mass-transfer-over-an-unsteady-stretching-s",signatures:"Stanford Shateyi, Fazle Mabood and Gerald Tendayi Marewo",authors:[{id:"16056",title:"Dr.",name:"Stanford",middleName:null,surname:"Shateyi",fullName:"Stanford Shateyi",slug:"stanford-shateyi"},{id:"185291",title:"Dr.",name:"Fazle",middleName:null,surname:"Mabood",fullName:"Fazle Mabood",slug:"fazle-mabood"},{id:"185293",title:"Dr.",name:"Gerald T",middleName:null,surname:"Marewo",fullName:"Gerald T Marewo",slug:"gerald-t-marewo"}]},{id:"51428",title:"Computational Fluid Dynamics in Turbulent Flow Applications",slug:"computational-fluid-dynamics-in-turbulent-flow-applications",signatures:"Alejandro Alonzo-García, Claudia del Carmen Gutiérrez-Torres and José Alfredo Jiménez-Bernal",authors:[{id:"185292",title:"Dr.",name:"Alejandro",middleName:null,surname:"Alonzo-García",fullName:"Alejandro Alonzo-García",slug:"alejandro-alonzo-garcia"},{id:"185294",title:"Dr.",name:"José Alfredo",middleName:null,surname:"Jiménez-Bernal",fullName:"José Alfredo Jiménez-Bernal",slug:"jose-alfredo-jimenez-bernal"},{id:"189760",title:"Dr.",name:"Claudia Del C.",middleName:null,surname:"Gutierrez-Torres",fullName:"Claudia Del C. Gutierrez-Torres",slug:"claudia-del-c.-gutierrez-torres"}]},{id:"51087",title:"Two-Fluid RANS-RSTM-PDF Model for Turbulent Particulate Flows",slug:"two-fluid-rans-rstm-pdf-model-for-turbulent-particulate-flows",signatures:"P. Lauk, A. Kartushinsky, M. Hussainov, A. Polonsky, Ü. Rudi, I. Shcheglov, S. Tisler and K.-E. Seegel",authors:[{id:"57704",title:"Dr.",name:"Alexander",middleName:"Ivanovich",surname:"Kartushinsky",fullName:"Alexander Kartushinsky",slug:"alexander-kartushinsky"},{id:"169397",title:"Dr.",name:"Medhat",middleName:null,surname:"Hussainov",fullName:"Medhat Hussainov",slug:"medhat-hussainov"},{id:"169398",title:"Dr.",name:"Igor",middleName:null,surname:"Shcheglov",fullName:"Igor Shcheglov",slug:"igor-shcheglov"},{id:"169399",title:"Dr.",name:"Sergei",middleName:null,surname:"Tisler",fullName:"Sergei Tisler",slug:"sergei-tisler"},{id:"188304",title:"Dr.",name:"Peep",middleName:null,surname:"Lauk",fullName:"Peep Lauk",slug:"peep-lauk"},{id:"188305",title:"Dr.",name:"Ulo",middleName:null,surname:"Rudi",fullName:"Ulo Rudi",slug:"ulo-rudi"},{id:"188306",title:"M.Sc.",name:"Karl",middleName:"Erik",surname:"Seegel",fullName:"Karl Seegel",slug:"karl-seegel"},{id:"188376",title:"Dr.",name:"Andrei",middleName:null,surname:"Polonscky",fullName:"Andrei Polonscky",slug:"andrei-polonscky"}]},{id:"51693",title:"Free Surface Flow Simulation Using VOF Method",slug:"free-surface-flow-simulation-using-vof-method",signatures:"Mohammad Javad Ketabdari",authors:[{id:"181430",title:"Dr.",name:"Mohammad Javad",middleName:null,surname:"Ketabdari",fullName:"Mohammad Javad Ketabdari",slug:"mohammad-javad-ketabdari"},{id:"191220",title:"Dr.",name:"Mohammad Javad",middleName:null,surname:"Ketabdari",fullName:"Mohammad Javad Ketabdari",slug:"mohammad-javad-ketabdari"}]},{id:"51748",title:"Transport and Mixing in Liquid Phase Using Large Eddy Simulation: A Review",slug:"transport-and-mixing-in-liquid-phase-using-large-eddy-simulation-a-review",signatures:"Juan M. Mejía, Amsini Sadiki, Farid Chejne and Alejandro Molina",authors:[{id:"180046",title:"Prof.",name:"Juan M.",middleName:null,surname:"Mejía",fullName:"Juan M. Mejía",slug:"juan-m.-mejia"},{id:"181375",title:"Prof.",name:"Amsini",middleName:null,surname:"Sadiki",fullName:"Amsini Sadiki",slug:"amsini-sadiki"},{id:"181377",title:"Prof.",name:"Farid",middleName:null,surname:"Chejne",fullName:"Farid Chejne",slug:"farid-chejne"},{id:"181378",title:"Prof.",name:"Alejandro",middleName:null,surname:"Molina",fullName:"Alejandro Molina",slug:"alejandro-molina"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"64099",title:"Comparison of Ethanol and Methanol Blending with Gasoline Using Engine Simulation",doi:"10.5772/intechopen.81776",slug:"comparison-of-ethanol-and-methanol-blending-with-gasoline-using-engine-simulation",body:'
1. Introduction
In the last years, the problem with crude oil depletion has arisen. Intensive research has been carried out to find out alternative to fossil fuels. Alternative fuels are derived from resources different from petroleum. When used in internal combustion engines (ICE), these fuels generate lower air pollutants compared to petrol fuel, and a majority of them are more economically beneficial compared to fossils fuels. They are also renewable. The most common fuels that are used as alternative fuels are natural gas, propane, methanol, ethanol, and hydrogen. Regarding engine operating with blended fuels, a lot of papers have been written about these blended fuels; but a small number of works have compared some of these fuels together in the same engine [1, 2, 3, 4]. Low contents of ethanol or methanol have been added to gasoline since at least the 1970s, when there was a reduction in oil supplies and scientists began searching for alternative energy carriers in order to replace petrol fuels. In the beginning, ethanol and methanol were thought to be the most attractive alcohols to be added to gasoline. Ethanol and methanol can be manufactured from natural products or waste materials, whereas gasoline fuel which is a nonrenewable energy resource cannot be manufactured [5, 6]. An important feature is that methanol and ethanol can be used without requiring any significant changes in the structure of the engine. Being part of the various alcohols, ethanol and methanol are known as the most suitable fuels for spark-ignition (SI) engines.
The use of blended fuels is crucial since many of these blends can be used in engines with the aim to improve its performance, efficiency, and emissions. The oxygenates are one of the most important fuel additives to improve fuel efficiency (organic oxygen-containing compounds). A few oxygenates have been used as fuel additives, such as ethanol, methanol, methyl tertiary butyl alcohol, and tertiary butyl ether [7]. The process of using oxygenates makes more oxygen available in the combustion process and has a great potential to reduce SI engine exhaust emissions.
Regarding the combustion process, the flash point and autoignition temperature of methanol and ethanol are higher than pure gasoline, which makes it safer for storage and transportation. The latent heat of ethanol of evaporation is three to five times higher than pure gasoline; this leads to increase the volumetric efficiency because temperature of the intake manifold is lower. The heating value of ethanol is lower than gasoline. Consequently, 1.6 times more alcohol fuel is needed to achieve the exact same energy output. The stoichiometric air-fuel ratio of ethanol is around two-third of the pure gasoline; therefore, for complete combustion, the needed amount of air is lesser for ethanol [8]. Ethanol has several advantages compared to gasoline, e.g., lowering of unburned HC emissions, CO, and much better antiknock characteristics [9]. Ethanol and methanol have a lot higher octane number compared to pure gasoline fuel [10]. This enables higher compression ratios of engines and, as a result, increases its thermal efficiency [11]. The production of methanol can be from natural gas at no great cost and is easy to blend with gasoline fuel. These properties of methanol make it as an attractive additive. Methanol is aggressive to some materials, like plastic components and some of the metals in the fuel system. When using methanol it is necessary that precautions had to be taken when handling it [12].
There are many publications with different blends of alcohols and gasoline fuel. For example, Palmer [13] examined the influence of blends of ethanol and gasoline in spark-ignition engine. The obtained results pointed out that ethanol addition (10%) leads to 5% increase in the engine power and 5% octane number increase for each 10% ethanol added. The result showed that 10% of ethanol addition to gasoline fuel lead to reduction the emissions of CO up to 30%. In another study, Bata et al. [9] examined different blends of ethanol and gasoline and discovered that ethanol reduced the UHC and CO emissions. The lowered CO emissions are caused by the oxygenated characteristic and wide flammability of ethanol. Other researchers [14] studied that the potentialities for ethanol production are equivalent to about 32% of the total gasoline consumption worldwide, when used in 85% ethanol in gasoline for a passenger vehicle. In another study, Shenghua et al. [15] examined a gasoline engine with various percentages of methanol blends (from 10 to 30%) in gasoline. The results showed that engine torque and power decreased, whereas the brake thermal efficiency improved with the increase of methanol percentage in the fuel blend. Other authors [16] have studied the influence of methanol-gasoline blends on the gasoline engine performance. The results showed that the highest brake mean effective pressure (BMEP) was obtained from 5% methanol-gasoline blend. In another study, Altun et al. [17] researched the influence of 5 and 10% methanol and ethanol blending in gasoline fuel on engine performance and emissions. The best result in emissions showed blended fuels. The HC emissions of E10 and M10 are reduced by 13 and 15% and the CO emissions by 10.6 and 9.8%, respectively. An increased CO2 emission for E10 and M10 was observed. The methanol and ethanol addition to gasoline showed an increase in the brake-specific fuel consumption (BSFC) and a decrease in break thermal efficiency compared to gasoline.
It can be seen in the literature survey that the exhaust emissions for ethanol-gasoline and methanol-gasoline blends are lower than that of pure gasoline fuel [9, 13, 14, 17]. The engine performance and exhaust emissions with ethanol-gasoline blends resemble those with methanol-gasoline blends.
From the reviewed literature, a conclusion was made that the exhaust emission and engine performance of various blends of methanol and ethanol in gasoline engines have not been investigated sufficiently. Therefore, the objective of this work is to investigate the effects of methanol-gasoline and ethanol-gasoline fuel blends on the performance and exhaust emissions of a gasoline engine under various engine speeds, comparing them with those of pure gasoline.
The simulation tools are the most used in recent years owing to the continuous increase in computational power. The use of engine simulations enables optimization of engine combustion, geometry, and operating characteristics toward improving specific fuel consumption and exhaust emissions and reducing engine development time and costs. Consequently, it can be expected that the use of engine simulations during engine construction will continue to increase. Engine modeling is a fruitful research area, and therefore many laboratories have their own engine thermodynamic models with varying degrees of complexity, scope, and ease to use [18].
Computer simulation is becoming an important tool for time and cost efficiency in engine’s development. The simulation results are challenging to be obtained experimentally. Using computational fluid dynamics (CFD) has allowed researchers to understand the flow behavior and quantify important flow parameters such as mass flow rates or pressure drops, under the condition that the CFD tools have been properly validated against experimental results. Many processes in the engine are three-dimensional; however, it requires greater knowledge and large computational time. Thus, simplified one-dimensional simulation is occasionally used. Hence, simulating the complex components by means of a three-dimensional code and modeling the rest of the system with a one-dimensional code are the right choice to save computational time, i.e., the ducts. This way, a coupling methodology between the one-dimensional and the three-dimensional codes in the respective interfaces is necessary and has become the aim of numerous authors [19, 20, 21].
2. Research methodology
The aim of the present chapter is to develop the one-dimensional model of four-stroke port fuel injection (PFI) gasoline engine for predicting the effect of methanol-gasoline (M0–M50) and ethanol-gasoline (E0–E50) addition to gasoline on the exhaust emissions and performance of gasoline engine. For this, simulation of gasoline SI engine (calibrated) as the basic operating condition and the laminar burning velocity cor relations of methanol-gasoline and ethanol-gasoline blends for calculating the changed combustion duration was used. The engine power, specific fuel consumption, and exhaust emissions were compared and discussed [22, 23].
2.1. Simulation setup
The one-dimensional SI engine model is created by using the AVL BOOST software and has been employed to examine the performance and emissions working on gasoline, ethanol-gasoline, and methanol-gasoline blends.
In Figure 1, PFIE symbolizes the engine, while C1–C4 is the number of cylinders of the SI engine. The cylinders are the main element in this model, because they have many very important parameters to settle: the internal geometry, bore, stroke, connecting rod, length and compression ratio, as well as the piston pin offset and the mean crankcase pressure. The measuring points are marked with MP1–MP18. PL1–PL4 symbolizes the plenum. System boundary stands for SB1 and SB2. CL1 represents the cleaner. R1–R10 stands for flow restrictions. CAT1 symbolizes catalyst and fuel injectors—I1–I4. The flow pipes are numbered 1–34.
Figure 1.
Schematic of the gasoline PFI engine model.
The calibrated gasoline engine model was described by Iliev [23], and its layout is shown in Figure 1 with engine specification shown in Table 1.
Engine parameters
Value
Bore
86 (mm)
Stroke
86 (mm)
Compression ratio
10.5
Connection rod length
143.5 (mm)
Number of cylinder
4
Piston pin offset
0 (mm)
Displacement
2000 (cc)
Intake valve open
20 BTDC (deg)
Intake valve close
70 ABDC (deg)
Exhaust valve open
50 BBDC (deg)
Exhaust valve close
30 ATDC (deg)
Piston surface area
5809 (mm2)
Cylinder surface area
7550 (mm2)
Number of stroke
4
Table 1.
Engine specification.
Table 2 presents a comparison between the properties of gasoline, ethanol, and methanol. As shown in Table 2, compared with gasoline and ethanol, methanol has a higher elemental oxygen content and a lower heating value, molecular weight, elemental carbon, hydrogen content, and stoichiometric air/fuel ratio (AFR).
Properties
Gasoline
Methanol
Ethanol
Chemical formula
C8H15
CH3OH
C2H5OH
Molecular weight
111.21
32.04
46.07
Oxygen content (wt%)
—
49.93
34.73
Carbon content (wt%)
86.3
37.5
52.2
Hydrogen content (wt%)
24.8
12.5
13.1
Stoichiometric AFR
14.5
6.43
8.94
Lower heating value (MJ/kg)
44.3
20
27
Heat of evaporation (kJ/kg)
305
1.178
840
Research octane number
96.5
112
111
Motor octane number
87.2
91
92
Vapor pressure (psi at 37.7 OC)
4.5
4.6
2
Destiny (g/cm3)
0.737
0.792
0.785
Normal boiling point (OC)
38–204
64
78
Autoignition temperature (OC)
246–280
470
365
Table 2.
Comparison of fuel properties.
2.2. Combustion model description
In this research, two-zone model of Vibe was chosen for the combustion simulation and analysis. The combustion chamber was divided into two regions: unburned gas region and burned gas regions [17]. For the burned charge and unburned charge, the first law of thermodynamics is applied:
E1
dmuuudα=−pcdVudα−∑dQWudα−hudmBdα−hBB,udmBB,udαE2
where dmu represents the change of the internal energy in the cylinder, pcdVda is the piston work, dQFda stands for the fuel heat input, dQWda is the wall heat loses, hudmbda represents the enthalpy flow from the unburned to the burned zone due to the conversion of a fresh charge to combustion products. The heat flux between the two zones is neglected. hBBdmBBda is the enthalpy flow due to blow-by, u and b in the subscript are unburned and burned gas.
Moreover, the sum of the zone volumes must be equal to the cylinder volume, and the sum of the volume changes must be equal to the cylinder volume change:
dVbdα+dVudα=dVdαE3
Vb+Vu=VE4
The amount of burned mixture at each time setup is obtained from the Vibe function. For all other terms, for instance, wall heat losses, etc., models similar to the single-zone models with an appropriate distribution on the two zones are used [24].
2.3. A description of exhaust emission model
In AVL BOOST, the model of formation on NOx is based on AVL List Gmbh [24], which incorporates the Zeldovich mechanism [25]. The rate of NOx production was obtained using Eq. (5):
rNO=CPPMCKM2,0.1−α2.r11+αAK2 +r41+AK4.E5
where α=CNO.actCNO.equ.1CKM, AK2=r1r2+r3, AK4=r4r5+r6.
In the above equation, CPPM represents post-processing multiplier, CKM denotes kinetic multiplier, C stands for molar concentration in equilibrium, and ri represents reaction rates of Zeldovich mechanism.
The NOx formation model in AVL Boost is based on Onorati et al. [26]:
rCO=CConstr1+r2.1−αE6
whereα=CCO.actCCO.equ.
In Eq. (6), C represents molar concentration in equilibrium and ri represents reaction rates based on the model.
The unburned HC has different sources. A complete description of HC formation still cannot be given, and the achievement of a reliable model within a thermodynamic approach is definitely prevented by the fundamental assumptions and the requirement of reduced computational times. Still, a phenomenological model which accounts for the main formation mechanisms and is able to capture the HC trends as function of the engine operating parameter may be proposed. The following important sources of unburned HC can be identified in SI engines [21]:
During the intake and compression stroke, fuel vapor is absorbed into the oil layer and deposits on the cylinder walls. The following desorption occurs when the cylinder pressure decreases during the expansion stroke and complete combustion cannot take place anymore.
A fraction of the charge enters the crevice volumes and is not burned since the flame quenches at the entrance.
Occasional complete misfire or partial burning takes place when combustion quality is poor.
Quench layers on the combustion chamber wall which are left as the flame extinguishes prior to reaching the walls.
The flow of fuel vapor into the exhaust system during valve overlap in gasoline engines.
The first two mechanisms and in particular the crevice formation are considered to be the most important and need to be accounted for in a thermodynamic model. Partial burn and quench layer effect cannot be physically described in a quasi-dimensional approach, but may be included by adopting tunable semiempirical correlations.
The formation of unburned HC in the crevices is described by assuming that the pressure in the cylinder and in the crevices is the same and that the temperature of the mass in the crevice volumes is equal to the piston temperature.
The mass in the crevices at any time is described by Eq. (7):
mcrevice=pVcreviceMRTpistonE7
In Eq. (7), mcrevice represents the mass of unburned charge in the crevice, p denotes cylinder pressure, Vcrevice stands for total crevice volume, M represents unburned molecular weight, Tpiston is the temperature of the piston, and R denotes gas constant.
The second important source of HC is the presence of lubricating oil in the fuel or on the walls of the combustion chamber. During the compression stroke, the fuel vapor pressure increases so, by Henry’s law, absorption occurs even if the oil was saturated during the intake. During combustion the concentration of fuel vapor in the burned gases goes to zero so the absorbed fuel vapor will desorb from the liquid oil into the burned gases. Fuel solubility is a positive function of the molecular weight, so the oil layer contributed to HC emissions depending on the different solubility of individual hydrocarbons in the lubricating oil.
The assumptions made in the development of the HC absorption/desorption are the following:
Fuel is constituted by a single hydrocarbon species, completely vaporized in the fresh mixture.
The oil film temperature is at the same as the cylinder wall.
Traverse flow across the oil film is negligible.
Oil is represented by squalane (C30H62), whose characteristics resemble those of the SAE5W20 lubricant.
Diffusion of the fuel in the oil film is the limiting factor, for the diffusion constant in the liquid phase which is 104 times smaller than the corresponding value in the gas phase.
The radial distribution of the fuel mass fraction in the oil film can be determined by solving the diffusion Eq. (8):
∂wF∂t−D∂2wF∂r2=0E8
In Eq. (8), wF represents fuel’s mass fraction in the oil film, t is the time, r stands for radial position in the oil film (distance from the wall), and D is relative (fuel-oil) diffusion coefficient.
3. Result and discussion
The present research focused on the performance and emission characteristics of the methanol and ethanol-gasoline blends. Various concentrations of the blends 0% methanol (ethanol) M0 (E0), 5% methanol (ethanol) M5 (E5), 10% methanol (ethanol) M10 (E10), 20% methanol (ethanol) M20 (E20), 30% methanol (ethanol) M30 (E30), 50% methanol (ethanol) M50 (E50), and 85% methanol (ethanol) M85 (E85) by volume were analyzed.
3.1. Engine performance characteristics
The results of the brake power and specific fuel consumption for ethanol-gasoline blended fuels at different engine speeds are shown on Figures 2 and 3.
Figure 2.
Influence of ethanol-gasoline blended fuels on brake power.
Figure 3.
Influence of ethanol-gasoline blended fuels on brake-specific fuel consumption.
The brake power is one of the important factors that determine the performance of an engine. The variation of brake power with speed was obtained at full load conditions for E5, E10, E20, E30, E50, and pure gasoline E0. The ethanol content in the blended fuel increased, and the brake power decreased for all engine speeds. The gasoline brake power was higher than E5–E50 for all engine speeds. The ethanol’s heat of evaporation is higher in comparison to gasoline fuel, providing air-fuel charge cooling and increasing the density of the charge. The blended fuel causes the equivalence ratio of blend approaches to stoichiometric condition which can lead to a better combustion. However, the ethanol heating value is lower compared to gasoline, and it can neutralize the previous positive effects. Consequently, a lower power output is obtained.
Figure 3 shows the changes of the BSFC for ethanol-gasoline blends under various engine speeds. The figure shows that the BSFC increased as the ethanol percentage increased. Heating value and stoichiometric air-fuel ratio are the smallest for these two fuels, which means that for specific air-fuel equivalence ratio, more fuel is needed. The highest specific fuel consumption is obtained at E50 ethanol-gasoline blend.
Moreover, there is a slight difference between the BSFC when using pure gasoline and when using blends (E5, E10, and E20). The lower energy content of blended fuels causes some increment in BSFC of the engine.
Figure 4 shows the influence of methanol-gasoline blended fuels on engine brake power. The variation of brake power with speed was obtained at full load conditions for M5, M10, M20, M30, M50, and pure gasoline M0. When the methanol content in the blended fuel was increased (M10, M20, and M30), there was not a significant increase in engine brake power.
Figure 4.
Influence of methanol-gasoline blended fuels on brake power.
The engine brake power may be due to the increase of the indicated mean effective pressure for higher methanol content blends. The methanol’s heat of evaporation is higher compared to that of gasoline, thus providing air-fuel charge cooling and increasing the density of the charge. Therefore, a higher power output is obtained. The engine brake power was higher in operation with gasoline in comparison to M50 for all engine speeds.
Figure 5 shows the variations of the BSFC for methanol-gasoline blended fuels under various engine speeds. As shown in this figure, the BSFC increased as the methanol percentage increased. This can be described with heating value, and stoichiometric air-fuel ratio is the smallest for these two fuels, which means that for specific air-fuel equivalence ratio, more fuel is needed. The specific fuel consumption of M50 methanol-gasoline blend was highest compared to those of gasoline for all engine speeds.
Figure 5.
Influence of methanol-gasoline blended fuels on engine brake power.
Furthermore, there is a small difference between the BSFC when using gasoline and when using methanol-gasoline blended fuels (M5–M30). As engine speed increased reaching 2000 rpm, the BSFC decreased reaching its minimum value.
The results of the brake power and specific fuel consumption for ethanol- and methanol-gasoline blended fuels at different engine speeds are presented in Figures 6 and 7.
Figure 6.
Effect of blended fuels on engine brake power.
Figure 7.
Influence of blended fuels on engine fuel consumption.
When there was an increase in the ethanol content in the blended fuel, the brake power decreased for all engine speeds. The brake power of gasoline fuel was higher than those of E5–E50. The heating value of ethanol is lower than pure gasoline fuel, and the heating value of the blends decreases with the increase of the ethanol percentage. Consequently, a lower power output is obtained [22, 23].
By increasing the percentage of methanol in the blends (M5 and M10), the brake power slightly increased, which can be explained by better combustion efficiency of oxygenated fuels. By increasing the methanol content in the blends (M30 and M50), the engine brake power decreased for all engine speeds. The blended fuel heating value decreases with the increase of the percentage of methanol. This results in a lower power output. The gasoline brake power was higher compared to blend M50.
Figure 7 shows the changes of the BSFC for blended fuels under different engine speeds. The BSFC increased as the ethanol and methanol percentage increased. The reason has been known—the heating value and stoichiometric air-fuel ratio are the smallest for this fuel, which means that more fuel is needed for specific air-fuel equivalence ratio. The highest specific fuel consumption is obtained at E50 (M50) blended fuel.
What is more, there is small difference between the BSFC when using pure gasoline and blended fuels (E5 (M5), E10 (M10), and E20 (M20)). The lower energy content of ethanol blended fuels makes some increment in BSFC.
3.2. Emission characteristics
The result of the ethanol-blended fuels on CO emissions is shown in Figure 8.
Figure 8.
Influence of ethanol-gasoline blended fuels on CO emissions.
A conclusion, which can be made by Figure 8, is that when ethanol content increases, the CO emission decreases. The reason for this could be explained with the enrichment of oxygen owing to the ethanol, in which an increase in the proportion of oxygen will promote the further oxidation of CO during the engine exhaust process. One of the other significant reasons for this reduction is that ethanol (C2H5OH) has less carbon than gasoline (C8H18).
The result of the ethanol gasoline blends on HC emissions is shown in Figure 9. The figure shows that when ethanol percentage increases, the HC concentration decreases. The HC emission decreases with the increase of the relative air-fuel ratio. The decrease of HC can be explained similarly to that of CO concentration described above.
Figure 9.
Influence of ethanol-gasoline blended fuels on HC emissions.
The effect of the ethanol gasoline blends on NOx emissions for various engine speeds is shown in Figure 10. When the combustion process is closer to stoichiometric, flame temperature increases. As a result, the NOx emissions are increased.
Figure 10.
Influence of ethanol-gasoline blended fuels on NOx emissions.
The effect of the methanol-gasoline blends on CO emissions for various engine speeds can be seen in Figure 11. When methanol percentage increases, the CO concentration decreases. This can be explained with the enrichment of oxygen because of the methanol and less carbon of methanol than gasoline.
Figure 11.
Influence of methanol-gasoline blended fuels on CO emissions.
The effect of the methanol-gasoline blends on HC emissions is visible in Figure 12. When methanol percentage increases, the HC concentration decreases. The concentration of HC emissions decreases with the increase of the relative air-fuel ratio. The reason for the decrease of HC concentration resembles that of ethanol.
Figure 12.
Influence of methanol-gasoline blended fuels on HC emissions.
The effect of the methanol-gasoline blends on NOx emissions can be seen in Figure 13. When methanol percentage increases, the NOx concentration increases. When combustion process is closer to stoichiometric, flame temperature increases and the NOx emissions increase as well.
Figure 13.
Influence of methanol-gasoline blended fuels on NOx emissions.
The effect of the ethanol- and methanol-gasoline blends on CO emissions can be viewed in Figure 14. By increasing the methanol and ethanol content in the blended fuel, the CO emission decreases. The reason can be the enrichment of oxygen because of the ethanol and methanol, in which an increase in the proportion of oxygen will promote the further oxidation of CO during the engine exhaust process. Another major reason for this reduction is that ethanol (C2H5OH) and methanol (CH3OH) have less carbon than gasoline (C8H18). The lowest CO emissions are obtained with blended fuel containing methanol (M50).
Figure 14.
Influence of ethanol- and methanol–gasoline blended fuels on CO emissions.
The effect of the ethanol- and methanol-gasoline blends on HC emissions is visible in Figure 15. When there is an increase in the ethanol and methanol percentage, the HC concentration decreases.
Figure 15.
Influence of blended fuels on HC and NOx emissions.
When the relative air-fuel ratio increases, the concentration of HC emissions decreases. The reason for the decrease in HC emissions is similar to that of CO described above. The comparison between the decrease in HC emissions and the blended fuels indicates that methanol is more effective than ethanol. The lowest HC emissions are obtained with methanol-blended fuel (M50). When more combustion is complete, it will result in lower HC emissions.
Figure 15 shows the influence of the blended fuels on NOx emissions. It is noticeable that when methanol and ethanol percentage increases up to 30% E30 (M30), the NOx emission increases, after which it decreases with increasing the percentage of the methanol (ethanol).
The reason is that the improved combustion results in increased temperature in combustion chamber. The higher methanol (ethanol) content in the blends lowers the temperature in combustion chamber. The lower temperature is due to:
Latent heat of evaporation of alcohols, which decreases the temperature in combustion chamber during the vaporization.
The more triatomic molecules are produced: the higher the gas heat capacity and the lower the combustion gas temperature will be. However, the low temperature in combustion chamber can also lead to an increment in the unburned combustion product.
4. Conclusions
The purpose of the present chapter is to demonstrate the influence of ethanol and methanol addition to gasoline on spark-ignition engine performance and emission characteristics. The summarized results from this study are the following:
With the increase of the percentage of ethanol in the blended fuel, the engine brake power decreased for various engine speeds.
With the increase of the percentage of methanol in the blends M5 and M10, the brake power slightly increased, and with the increase of the percentage of methanol in the blends M30 and M50, the brake power decreased.
As the ethanol (methanol) percentage increased, the BSFC increased. The blended fuels show higher BSFC and lower engine brake power than pure gasoline. Furthermore, there is a slight difference between the BSFC in comparison of gasoline and gasoline blended fuels (E5, E10, and E20 and M5, M10, and M20).
When there is an increase in ethanol and methanol percentage, the CO and HC concentration decreases. The lowest CO and HC emissions are obtained with blended fuel containing methanol (M50).
Increasing the percentage of ethanol and methanol leads to a significant increase in NOx emissions.
When there is an increase in the ethanol and methanol percentage up to 30% E30 (M30), there is an increase in the NOx concentration, followed by a decrease, after which it decreases with increasing ethanol (methanol) percentage. The lowest NOx emissions are obtained with gasoline.
Acknowledgments
The present chapter has been written with the Project No 2018-RU-07’s financial assistance. We are also eternally grateful to AVL-AST, Graz, Austria, for granting the use of AVL BOOST under the university partnership program.
\n',keywords:"alternative fuels, ethanol blends, methanol blends, engine simulation, spark-ignition engine, emissions",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/64099.pdf",chapterXML:"https://mts.intechopen.com/source/xml/64099.xml",downloadPdfUrl:"/chapter/pdf-download/64099",previewPdfUrl:"/chapter/pdf-preview/64099",totalDownloads:1475,totalViews:1569,totalCrossrefCites:3,dateSubmitted:"January 29th 2018",dateReviewed:"October 1st 2018",datePrePublished:"November 5th 2018",datePublished:null,dateFinished:null,readingETA:"0",abstract:"During the last years, concerns regarding climate change, decline of energy security, and hydrocarbon reserves have resulted in a wide interest in renewable alternative sources for transportation fuels. Methanol and ethanol have been possible candidates as alternative fuels for the internal combustion engines because they are liquid and have several physical and combustion properties which resemble those of gasoline. Therefore, the aim of this study is to develop the one-dimensional model of a gasoline engine for predicting the effect of various fuel types on engine performances, specific fuel consumption, and emissions. Commercial software AVL BOOST was used to examine the engine characteristics for different blends of methanol, ethanol, and gasoline (by volume). A comparison was made between the results gained from the engine simulation of different fuel blends and those of gasoline. They show that when blended fuel was used, the engine brake power decreased and the BSFC increased compared to those of gasoline fuel. When blended fuel increases, the CO and HC emissions decrease, and there is a major increase in NOx emissions when blended fuel increases up to 30% M30 (E30). Increase in the percentage of ethanol and methanol leads to a significant increase in NOx emissions.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/64099",risUrl:"/chapter/ris/64099",signatures:"Simeon Iliev",book:{id:"7514",title:"Biofuels",subtitle:"Challenges and opportunities",fullTitle:"Biofuels - Challenges and opportunities",slug:"biofuels-challenges-and-opportunities",publishedDate:"March 13th 2019",bookSignature:"Mansour Al Qubeissi",coverURL:"https://cdn.intechopen.com/books/images_new/7514.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"241686",title:"Dr.",name:"Mansour",middleName:null,surname:"Al Qubeissi",slug:"mansour-al-qubeissi",fullName:"Mansour Al Qubeissi"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Research methodology",level:"1"},{id:"sec_2_2",title:"2.1. Simulation setup",level:"2"},{id:"sec_3_2",title:"2.2. Combustion model description",level:"2"},{id:"sec_4_2",title:"2.3. A description of exhaust emission model",level:"2"},{id:"sec_6",title:"3. Result and discussion",level:"1"},{id:"sec_6_2",title:"3.1. Engine performance characteristics",level:"2"},{id:"sec_7_2",title:"3.2. Emission characteristics",level:"2"},{id:"sec_9",title:"4. Conclusions",level:"1"},{id:"sec_10",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Iliev S. Investigation of N-butanol blending with gasoline using a 1-d engine model. In: Special Session on Sustainable Mobility Solutions: Vehicle and Traffic Simulation, On-Road Trials and EV Charging. 2017. pp. 385-391. DOI: 10.5220/0006284703850391'},{id:"B2",body:'Melo T, Machado G, Machado RT, Pereira Belchior CR Jr, Pereira PP. Thermodynamic modeling of compression, combustion and expansion processes of gasoline, ethanol and natural gas with experimental validation on a flexible fuel engine. In: SAE World Congress. 2007. 2007-24-0035'},{id:"B3",body:'Varol Y, Oner C, Oztop HF, Altun S. Comparison of methanol, ethanol, or n-butanol blending with unleaded gasoline on exhaust emissions of an SI engine. Energy Sources Part A Recovery Utilization and Environmental Effects. 2014;36:938-948'},{id:"B4",body:'Pourkhesalian A, Shamekhi A, Salimi F. Alternative fuel and gasoline in an SI engine: A comparative study of performance and emissions characteristics. Fuel. 2010;89:1056-1063'},{id:"B5",body:'Chen C, Rao P, Delfino J. Oxygenated fuel induced to solvent effect on the dissolution of polynuclear aromatic hydrocarbons from contemned soil. Chemosphere. 2005;60:1572-1582'},{id:"B6",body:'Canaksi M, Ozsezen AN, Alptekin E. Impact of alcohol-gasoline fuel blends on exhaust emission on an SI engine. Renewable Energy. 2013;52:111-117'},{id:"B7",body:'Cavalcante Cordeiro de Melo T, Bastos Machado G, Machado RT, Pereira Belchior CR Jr, Pereira PP. Thermodynamic modeling of compression, combustion and expansion processes of gasoline, ethanol and natural gas with experimental validation on a flexible fuel engine. In: SAE World Congress. 2007. 2007-24-0035'},{id:"B8",body:'Pikunas A, Pukalskas S, Grabys J. Influence of composition of gasoline-ethanol blends on parameters of internal combustion engines. Journal of KONES Internal Combustion Engines. 2003;10:3-4'},{id:"B9",body:'Bata R, Elord A, Rice R. Emissions from IC engines fueled with alcohol–gasoline blends: A literature review. Journal of Engineering for Gas Turbines and Power. 1989;111:424-431'},{id:"B10",body:'Silva R, Cataluna R, Menezes EW, Samios D, Piatnicki CMS. Effect of additives on the antiknock properties and Reid vapor pressure of gasoline. Fuel. 2005;84:951-959'},{id:"B11",body:'Raveendran K, Ganesh A. Heating value of biomass and biomass pyrolysis products. Fuel. 1996;75:1715-1720'},{id:"B12",body:'Egebäck K, Henke M, Rehnlund B, Wallin M, Westerholm R. Blending of ethanol in gasoline for spark ignition engines—problem inventory and evaporative measurements. Rapport MTC 5407. 2005'},{id:"B13",body:'Palmer F. Vehicle performance of gasoline containing oxygenates. In: International Conference on Petroleum Based and Automotive Applications. London, UK: Institution of Mechanical Engineers Conference Publications, MEP; 1986. pp. 33-46'},{id:"B14",body:'Kim S, Dale BE. Global potential bioethanol production from wasted crops and crop residues. Biomass and Bioenergy. 2004;26(4):361-375'},{id:"B15",body:'Shenghua L, Clemente ERC, Tiegang H, Yanjv W. Study of spark ignition engine fueled with methanol/gasoline fuel blends. Applied Thermal Engineering. 2007;27:1904-1910'},{id:"B16",body:'Bilgin A, Sezer I. Effects of methanol addition to gasoline on the performance and fuel cost of a spark ignition engine. Energy & Fuels. 2008;22:2782-2788'},{id:"B17",body:'Altun S, Oztop H, Oner C, Varol Y. Exhaust emissions of methanol and ethanol-unleaded gasoline blends in a spark ignition engine. Thermal Science. 2013;17(1):291-297'},{id:"B18",body:'Shamekhi A, Khtibzade N, Shamekhi AH. Performance and emissions characteristics of a bi-fuel SI engine fueled by CNG and gasoline. ASME Paper. 2006. ICES2006-1387'},{id:"B19",body:'Onorati A, Montenegro G, D’Errico G. Prediction of the attenuation characteristics of IC engine silencers by 1-D and multi-D simulation models. SAE Technical Paper Series 2006. Tech. Rep. 2006-01-1541. 2006. DOI: 10.4271/2006-01-1541'},{id:"B20",body:'Montenegro G, Onorati A. Modeling of silencers for IC engine intake and exhaust systems by means of an integrated 1D-multiD approach. SAE International Journal of Engines. 2009;1(1):466. DOI: 10.4271/2008-01-0677'},{id:"B21",body:'Montenegro G, Onorati A, Piscaglia F, D’Errico G. Integrated 1D-multi-D fluid dynamic models for the simulation of ICE intake and exhaust systems. SAE Technical Paper Series 2007. Tech. Rep. 2007-01-0495. 2007. DOI: 10.4271/2007-01-0495'},{id:"B22",body:'Iliev S. Developing of a 1-D combustion model and study of engine performance and exhaust emissions using ethanol-gasoline blends. In: IAENG Transaction of Engineering Technologies. Netherlands: Springer; 2014, unpublished'},{id:"B23",body:'Iliev S. Developing of a 1-D combustion model and study of engine characteristics using ethanol-gasoline blends. In: Proceedings of the World Congress on Engineers. Vol. II. 2014, WCE 2014/ 978-988-19253-5-0'},{id:"B24",body:'AVL List Gmbh. AVL Boost—Theory. 2013'},{id:"B25",body:'Bowman C. Kinetics of pollutant formation and destruction in combustion. Progress in Energy and Combustion Science. 1975;1(1):33-45'},{id:"B26",body:'Onorati A, Ferrari G, D’Errico G. 1D unsteady flows with chemical reactions in the exhaust duct-system of S.I. engines: Predictions and experiments. SAE Paper No. 2001-01-0939'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Simeon Iliev",address:"spi@uni-ruse.bg",affiliation:'
Department of Engines and Vehicles, University of Ruse, Ruse, Bulgaria
'}],corrections:null},book:{id:"7514",title:"Biofuels",subtitle:"Challenges and opportunities",fullTitle:"Biofuels - Challenges and opportunities",slug:"biofuels-challenges-and-opportunities",publishedDate:"March 13th 2019",bookSignature:"Mansour Al Qubeissi",coverURL:"https://cdn.intechopen.com/books/images_new/7514.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"241686",title:"Dr.",name:"Mansour",middleName:null,surname:"Al Qubeissi",slug:"mansour-al-qubeissi",fullName:"Mansour Al Qubeissi"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},profile:{item:{id:"193065",title:"Dr.",name:"Maoxi",middleName:null,surname:"Tian",email:"tmx015@126.com",fullName:"Maoxi Tian",slug:"maoxi-tian",position:null,biography:null,institutionString:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"1",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:null},booksEdited:[],chaptersAuthored:[{title:"Fitting Truncated Mode Regression Model by Simulated Annealing",slug:"fitting-truncated-mode-regression-model-by-simulated-annealing",abstract:"Like mean, median, and standard deviation, mode as the value that appears most often in a set of data is an important feature of a distribution. The numerical value of the mode is the same as that of the mean and median in a symmetric distribution but may be very different in a highly skewed distribution. Mode regression, which models the relationship between the mode of a dependent variable and some covariates, was first introduced by Lee in terms of truncated dependent variables. Some modifications of the truncated mode regression have been proposed recently. However, little progress is made on the computation or algorithm of fitting a mode regression due to an NP-hard optimization problem. In this paper we first introduce the popular simulated annealing (SA) to solve the truncated mode regression optimization. Experiments with simulations compare favorably to SA. Then, a mode regression with the proposed algorithm is applied to explore the typical income structure of China. We also compare the income returns to gender, education, experience, job sector, and district between the majority of workers with typical income and the workers with mean, middle income via comparison between mode regression, mean regression, and median regression.",signatures:"Maoxi Tian, Jian He and Keming Yu",authors:[{id:"187310",title:"Prof.",name:"Keming",surname:"Yu",fullName:"Keming Yu",slug:"keming-yu",email:"keming.yu@brunel.ac.uk"},{id:"193065",title:"Dr.",name:"Maoxi",surname:"Tian",fullName:"Maoxi Tian",slug:"maoxi-tian",email:"tmx015@126.com"}],book:{title:"Computational Optimization in Engineering",slug:"computational-optimization-in-engineering-paradigms-and-applications",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"142583",title:"Dr.",name:"Bithin",surname:"Datta",slug:"bithin-datta",fullName:"Bithin Datta",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"147357",title:"Prof.",name:"Makoto",surname:"Yasuda",slug:"makoto-yasuda",fullName:"Makoto Yasuda",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"186473",title:"Dr.",name:"Yoel",surname:"Tenne",slug:"yoel-tenne",fullName:"Yoel Tenne",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"187216",title:"Dr.",name:"Yang",surname:"Xiang",slug:"yang-xiang",fullName:"Yang Xiang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Philip Morris International (Germany)",institutionURL:null,country:{name:"Germany"}}},{id:"187218",title:"Dr.",name:"Florian",surname:"Martin",slug:"florian-martin",fullName:"Florian Martin",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"187219",title:"Mr.",name:"Sylvain",surname:"Gubian",slug:"sylvain-gubian",fullName:"Sylvain Gubian",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"187310",title:"Prof.",name:"Keming",surname:"Yu",slug:"keming-yu",fullName:"Keming Yu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Brunel University London",institutionURL:null,country:{name:"United Kingdom"}}},{id:"187491",title:"Dr.",name:"Augustine",surname:"Wong",slug:"augustine-wong",fullName:"Augustine Wong",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"188338",title:"Prof.",name:"Barry",surname:"Smith",slug:"barry-smith",fullName:"Barry Smith",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"194066",title:"Ph.D. Student",name:"Mahsa",surname:"Amirabdollahian",slug:"mahsa-amirabdollahian",fullName:"Mahsa Amirabdollahian",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"open-access-funding-funders-list",title:"List of Funders by Country",intro:"
If your research is financed through any of the below-mentioned funders, please consult their Open Access policies or grant ‘terms and conditions’ to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
\n\n
IMPORTANT: You must be a member or grantee of the listed funders in order to apply for their Open Access publication funds. Do not attempt to contact the funders if this is not the case.
",metaTitle:"List of Funders by Country",metaDescription:"If your research is financed through any of the below-mentioned funders, please consult their Open Access policies or grant ‘terms and conditions’ to explore ways to cover your publication costs (also accessible by clicking on the link in their title).",metaKeywords:null,canonicalURL:"/page/open-access-funding-funders-list",contentRaw:'[{"type":"htmlEditorComponent","content":"
UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
Wellcome Trust (Funding available only to Wellcome-funded researchers/grantees)
\n
\n'}]},successStories:{items:[]},authorsAndEditors:{filterParams:{sort:"featured,name"},profiles:[{id:"6700",title:"Dr.",name:"Abbass A.",middleName:null,surname:"Hashim",slug:"abbass-a.-hashim",fullName:"Abbass A. Hashim",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/6700/images/1864_n.jpg",biography:"Currently I am carrying out research in several areas of interest, mainly covering work on chemical and bio-sensors, semiconductor thin film device fabrication and characterisation.\nAt the moment I have very strong interest in radiation environmental pollution and bacteriology treatment. The teams of researchers are working very hard to bring novel results in this field. I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. I have served as the editor for many books, been a member of the editorial board in science journals, have published many papers and hold many patents.",institutionString:null,institution:{name:"Sheffield Hallam University",country:{name:"United Kingdom"}}},{id:"54525",title:"Prof.",name:"Abdul Latif",middleName:null,surname:"Ahmad",slug:"abdul-latif-ahmad",fullName:"Abdul Latif Ahmad",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"20567",title:"Prof.",name:"Ado",middleName:null,surname:"Jorio",slug:"ado-jorio",fullName:"Ado Jorio",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidade Federal de Minas Gerais",country:{name:"Brazil"}}},{id:"47940",title:"Dr.",name:"Alberto",middleName:null,surname:"Mantovani",slug:"alberto-mantovani",fullName:"Alberto Mantovani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"12392",title:"Mr.",name:"Alex",middleName:null,surname:"Lazinica",slug:"alex-lazinica",fullName:"Alex Lazinica",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/12392/images/7282_n.png",biography:"Alex Lazinica is the founder and CEO of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). I am a Reviewer for several refereed journals and international conferences, such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Industrial Electronics, Optic Letters, Measurement Science Review, and also a member of the International Advisory Committee for 2012 IEEE Business Engineering and Industrial Applications and 2012 IEEE Symposium on Business, Engineering and Industrial Applications.",institutionString:null,institution:{name:"Joseph Fourier University",country:{name:"France"}}},{id:"55578",title:"Dr.",name:"Antonio",middleName:null,surname:"Jurado-Navas",slug:"antonio-jurado-navas",fullName:"Antonio Jurado-Navas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/55578/images/4574_n.png",biography:"Antonio Jurado-Navas received the M.S. degree (2002) and the Ph.D. degree (2009) in Telecommunication Engineering, both from the University of Málaga (Spain). He first worked as a consultant at Vodafone-Spain. From 2004 to 2011, he was a Research Assistant with the Communications Engineering Department at the University of Málaga. In 2011, he became an Assistant Professor in the same department. From 2012 to 2015, he was with Ericsson Spain, where he was working on geo-location\ntools for third generation mobile networks. Since 2015, he is a Marie-Curie fellow at the Denmark Technical University. His current research interests include the areas of mobile communication systems and channel modeling in addition to atmospheric optical communications, adaptive optics and statistics",institutionString:null,institution:{name:"University of Malaga",country:{name:"Spain"}}}],filtersByRegion:[{group:"region",caption:"North America",value:1,count:5766},{group:"region",caption:"Middle and South America",value:2,count:5228},{group:"region",caption:"Africa",value:3,count:1717},{group:"region",caption:"Asia",value:4,count:10370},{group:"region",caption:"Australia and Oceania",value:5,count:897},{group:"region",caption:"Europe",value:6,count:15791}],offset:12,limit:12,total:118192},chapterEmbeded:{data:{}},editorApplication:{success:null,errors:{}},ofsBooks:{filterParams:{},books:[{type:"book",id:"8969",title:"Deserts and Desertification",subtitle:null,isOpenForSubmission:!0,hash:"4df95c7f295de7f6003e635d9a309fe9",slug:null,bookSignature:"Dr. Yajuan Zhu, Dr. Qinghong Luo and Dr. Yuguo Liu",coverURL:"https://cdn.intechopen.com/books/images_new/8969.jpg",editedByType:null,editors:[{id:"180427",title:"Dr.",name:"Yajuan",surname:"Zhu",slug:"yajuan-zhu",fullName:"Yajuan Zhu"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8977",title:"Protein Kinase - New Opportunities, Challenges and Future Perspectives",subtitle:null,isOpenForSubmission:!0,hash:"6d200cc031706a565b554fdb1c478901",slug:null,bookSignature:"Dr. Rajesh Kumar Singh",coverURL:"https://cdn.intechopen.com/books/images_new/8977.jpg",editedByType:null,editors:[{id:"329385",title:"Dr.",name:"Rajesh",surname:"Singh",slug:"rajesh-singh",fullName:"Rajesh Singh"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9659",title:"Fibroblasts - Advances in Cancer, Autoimmunity and Inflammation",subtitle:null,isOpenForSubmission:!0,hash:"926fa6446f6befbd363fc74971a56de2",slug:null,bookSignature:"Ph.D. Mojca Frank Bertoncelj and Ms. Katja Lakota",coverURL:"https://cdn.intechopen.com/books/images_new/9659.jpg",editedByType:null,editors:[{id:"328755",title:"Ph.D.",name:"Mojca",surname:"Frank Bertoncelj",slug:"mojca-frank-bertoncelj",fullName:"Mojca Frank Bertoncelj"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9662",title:"Vegetation Index and Dynamics",subtitle:null,isOpenForSubmission:!0,hash:"0abf2a59ee63fc1ba4fb64d77c9b1be7",slug:null,bookSignature:"Dr. Eusebio Cano Carmona, Dr. Ricardo Quinto Canas, Dr. Ana Cano Ortiz and Dr. Carmelo Maria Musarella",coverURL:"https://cdn.intechopen.com/books/images_new/9662.jpg",editedByType:null,editors:[{id:"87846",title:"Dr.",name:"Eusebio",surname:"Cano Carmona",slug:"eusebio-cano-carmona",fullName:"Eusebio Cano Carmona"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9667",title:"Neuroimmunology",subtitle:null,isOpenForSubmission:!0,hash:"9cf0e8203ce088c0b84add014fd8d382",slug:null,bookSignature:"Prof. Robert Weissert",coverURL:"https://cdn.intechopen.com/books/images_new/9667.jpg",editedByType:null,editors:[{id:"79343",title:"Prof.",name:"Robert",surname:"Weissert",slug:"robert-weissert",fullName:"Robert Weissert"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9816",title:"Idiopathic Pulmonary Fibrosis",subtitle:null,isOpenForSubmission:!0,hash:"365bb9762ba33db2d07e677690af1772",slug:null,bookSignature:"Dr. Salim Surani and Dr. Venkat Rajasurya",coverURL:"https://cdn.intechopen.com/books/images_new/9816.jpg",editedByType:null,editors:[{id:"15654",title:"Dr.",name:"Salim",surname:"Surani",slug:"salim-surani",fullName:"Salim Surani"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10176",title:"Microgrids and Local Energy Systems",subtitle:null,isOpenForSubmission:!0,hash:"c32b4a5351a88f263074b0d0ca813a9c",slug:null,bookSignature:"Prof. Nick Jenkins",coverURL:"https://cdn.intechopen.com/books/images_new/10176.jpg",editedByType:null,editors:[{id:"55219",title:"Prof.",name:"Nick",surname:"Jenkins",slug:"nick-jenkins",fullName:"Nick Jenkins"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10218",title:"Flagellar Motility in Cells",subtitle:null,isOpenForSubmission:!0,hash:"5fcc15570365a82d9f2c4816f4e0ee2e",slug:null,bookSignature:"Prof. Yusuf Bozkurt",coverURL:"https://cdn.intechopen.com/books/images_new/10218.jpg",editedByType:null,editors:[{id:"90846",title:"Prof.",name:"Yusuf",surname:"Bozkurt",slug:"yusuf-bozkurt",fullName:"Yusuf Bozkurt"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10231",title:"Proton Therapy",subtitle:null,isOpenForSubmission:!0,hash:"f4a9009287953c8d1d89f0fa9b7597b0",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/10231.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10270",title:"Fog Computing",subtitle:null,isOpenForSubmission:!0,hash:"54853b3034f0348a6157b5591f8d95f3",slug:null,bookSignature:"Dr. Isiaka Ajewale Alimi, Dr. Nelson Muga, Dr. Qin Xin and Dr. Paulo P. Monteiro",coverURL:"https://cdn.intechopen.com/books/images_new/10270.jpg",editedByType:null,editors:[{id:"208236",title:"Dr.",name:"Isiaka",surname:"Alimi",slug:"isiaka-alimi",fullName:"Isiaka Alimi"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10343",title:"Ocular Hypertension",subtitle:null,isOpenForSubmission:!0,hash:"0ff71cc7e0d9f394f41162c0c825588a",slug:null,bookSignature:"Prof. Michele Lanza",coverURL:"https://cdn.intechopen.com/books/images_new/10343.jpg",editedByType:null,editors:[{id:"240088",title:"Prof.",name:"Michele",surname:"Lanza",slug:"michele-lanza",fullName:"Michele Lanza"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10370",title:"Advances in Fundamental and Applied Research on Spatial Audio",subtitle:null,isOpenForSubmission:!0,hash:"f16232a481c08a05cc191ac64cf2c69e",slug:null,bookSignature:"Dr. Brian FG Katz and Dr. Piotr Majdak",coverURL:"https://cdn.intechopen.com/books/images_new/10370.jpg",editedByType:null,editors:[{id:"278731",title:"Dr.",name:"Brian FG",surname:"Katz",slug:"brian-fg-katz",fullName:"Brian FG Katz"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],filtersByTopic:[{group:"topic",caption:"Agricultural and Biological Sciences",value:5,count:16},{group:"topic",caption:"Biochemistry, Genetics and Molecular Biology",value:6,count:4},{group:"topic",caption:"Business, Management and Economics",value:7,count:1},{group:"topic",caption:"Chemistry",value:8,count:8},{group:"topic",caption:"Computer and Information Science",value:9,count:6},{group:"topic",caption:"Earth and Planetary Sciences",value:10,count:7},{group:"topic",caption:"Engineering",value:11,count:16},{group:"topic",caption:"Environmental Sciences",value:12,count:2},{group:"topic",caption:"Immunology and Microbiology",value:13,count:3},{group:"topic",caption:"Materials Science",value:14,count:5},{group:"topic",caption:"Mathematics",value:15,count:1},{group:"topic",caption:"Medicine",value:16,count:24},{group:"topic",caption:"Neuroscience",value:18,count:1},{group:"topic",caption:"Pharmacology, Toxicology and Pharmaceutical Science",value:19,count:3},{group:"topic",caption:"Physics",value:20,count:3},{group:"topic",caption:"Psychology",value:21,count:4},{group:"topic",caption:"Robotics",value:22,count:1},{group:"topic",caption:"Social Sciences",value:23,count:3},{group:"topic",caption:"Technology",value:24,count:1},{group:"topic",caption:"Veterinary Medicine and Science",value:25,count:1}],offset:12,limit:12,total:191},popularBooks:{featuredBooks:[{type:"book",id:"9385",title:"Renewable Energy",subtitle:"Technologies and Applications",isOpenForSubmission:!1,hash:"a6b446d19166f17f313008e6c056f3d8",slug:"renewable-energy-technologies-and-applications",bookSignature:"Tolga Taner, Archana Tiwari and Taha Selim Ustun",coverURL:"https://cdn.intechopen.com/books/images_new/9385.jpg",editors:[{id:"197240",title:"Associate Prof.",name:"Tolga",middleName:null,surname:"Taner",slug:"tolga-taner",fullName:"Tolga Taner"}],equalEditorOne:{id:"186791",title:"Dr.",name:"Archana",middleName:null,surname:"Tiwari",slug:"archana-tiwari",fullName:"Archana Tiwari",profilePictureURL:"https://mts.intechopen.com/storage/users/186791/images/system/186791.jpg",biography:"Dr. Archana Tiwari is Associate Professor at Amity University, India. Her research interests include renewable sources of energy from microalgae and further utilizing the residual biomass for the generation of value-added products, bioremediation through microalgae and microbial consortium, antioxidative enzymes and stress, and nutraceuticals from microalgae. She has been working on algal biotechnology for the last two decades. She has published her research in many international journals and has authored many books and chapters with renowned publishing houses. She has also delivered talks as an invited speaker at many national and international conferences. Dr. Tiwari is the recipient of several awards including Researcher of the Year and Distinguished Scientist.",institutionString:"Amity University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Amity University",institutionURL:null,country:{name:"India"}}},equalEditorTwo:{id:"197609",title:"Prof.",name:"Taha Selim",middleName:null,surname:"Ustun",slug:"taha-selim-ustun",fullName:"Taha Selim Ustun",profilePictureURL:"https://mts.intechopen.com/storage/users/197609/images/system/197609.jpeg",biography:"Dr. Taha Selim Ustun received a Ph.D. in Electrical Engineering from Victoria University, Melbourne, Australia. He is a researcher with the Fukushima Renewable Energy Institute, AIST (FREA), where he leads the Smart Grid Cybersecurity Laboratory. Prior to that, he was a faculty member with the School of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. His current research interests include power systems protection, communication in power networks, distributed generation, microgrids, electric vehicle integration, and cybersecurity in smart grids. He serves on the editorial boards of IEEE Access, IEEE Transactions on Industrial Informatics, Energies, Electronics, Electricity, World Electric Vehicle and Information journals. Dr. Ustun is a member of the IEEE 2004 and 2800, IEC Renewable Energy Management WG 8, and IEC TC 57 WG17. He has been invited to run specialist courses in Africa, India, and China. He has delivered talks for the Qatar Foundation, the World Energy Council, the Waterloo Global Science Initiative, and the European Union Energy Initiative (EUEI). His research has attracted funding from prestigious programs in Japan, Australia, the European Union, and North America.",institutionString:"Fukushima Renewable Energy Institute, AIST (FREA)",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"National Institute of Advanced Industrial Science and Technology",institutionURL:null,country:{name:"Japan"}}},equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"10065",title:"Wavelet Theory",subtitle:null,isOpenForSubmission:!1,hash:"d8868e332169597ba2182d9b004d60de",slug:"wavelet-theory",bookSignature:"Somayeh Mohammady",coverURL:"https://cdn.intechopen.com/books/images_new/10065.jpg",editors:[{id:"109280",title:"Dr.",name:"Somayeh",middleName:null,surname:"Mohammady",slug:"somayeh-mohammady",fullName:"Somayeh Mohammady"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9644",title:"Glaciers and the Polar Environment",subtitle:null,isOpenForSubmission:!1,hash:"e8cfdc161794e3753ced54e6ff30873b",slug:"glaciers-and-the-polar-environment",bookSignature:"Masaki Kanao, Danilo Godone and Niccolò Dematteis",coverURL:"https://cdn.intechopen.com/books/images_new/9644.jpg",editors:[{id:"51959",title:"Dr.",name:"Masaki",middleName:null,surname:"Kanao",slug:"masaki-kanao",fullName:"Masaki Kanao"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8985",title:"Natural Resources Management and Biological Sciences",subtitle:null,isOpenForSubmission:!1,hash:"5c2e219a6c021a40b5a20c041dea88c4",slug:"natural-resources-management-and-biological-sciences",bookSignature:"Edward R. Rhodes and Humood Naser",coverURL:"https://cdn.intechopen.com/books/images_new/8985.jpg",editors:[{id:"280886",title:"Prof.",name:"Edward R",middleName:null,surname:"Rhodes",slug:"edward-r-rhodes",fullName:"Edward R Rhodes"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9671",title:"Macrophages",subtitle:null,isOpenForSubmission:!1,hash:"03b00fdc5f24b71d1ecdfd75076bfde6",slug:"macrophages",bookSignature:"Hridayesh Prakash",coverURL:"https://cdn.intechopen.com/books/images_new/9671.jpg",editors:[{id:"287184",title:"Dr.",name:"Hridayesh",middleName:null,surname:"Prakash",slug:"hridayesh-prakash",fullName:"Hridayesh Prakash"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9313",title:"Clay Science and Technology",subtitle:null,isOpenForSubmission:!1,hash:"6fa7e70396ff10620e032bb6cfa6fb72",slug:"clay-science-and-technology",bookSignature:"Gustavo Morari Do Nascimento",coverURL:"https://cdn.intechopen.com/books/images_new/9313.jpg",editors:[{id:"7153",title:"Prof.",name:"Gustavo",middleName:null,surname:"Morari Do Nascimento",slug:"gustavo-morari-do-nascimento",fullName:"Gustavo Morari Do Nascimento"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9888",title:"Nuclear Power Plants",subtitle:"The Processes from the Cradle to the Grave",isOpenForSubmission:!1,hash:"c2c8773e586f62155ab8221ebb72a849",slug:"nuclear-power-plants-the-processes-from-the-cradle-to-the-grave",bookSignature:"Nasser Awwad",coverURL:"https://cdn.intechopen.com/books/images_new/9888.jpg",editors:[{id:"145209",title:"Prof.",name:"Nasser",middleName:"S",surname:"Awwad",slug:"nasser-awwad",fullName:"Nasser Awwad"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9027",title:"Human Blood Group Systems and Haemoglobinopathies",subtitle:null,isOpenForSubmission:!1,hash:"d00d8e40b11cfb2547d1122866531c7e",slug:"human-blood-group-systems-and-haemoglobinopathies",bookSignature:"Osaro Erhabor and Anjana Munshi",coverURL:"https://cdn.intechopen.com/books/images_new/9027.jpg",editors:[{id:"35140",title:null,name:"Osaro",middleName:null,surname:"Erhabor",slug:"osaro-erhabor",fullName:"Osaro Erhabor"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7841",title:"New Insights Into Metabolic Syndrome",subtitle:null,isOpenForSubmission:!1,hash:"ef5accfac9772b9e2c9eff884f085510",slug:"new-insights-into-metabolic-syndrome",bookSignature:"Akikazu Takada",coverURL:"https://cdn.intechopen.com/books/images_new/7841.jpg",editors:[{id:"248459",title:"Dr.",name:"Akikazu",middleName:null,surname:"Takada",slug:"akikazu-takada",fullName:"Akikazu Takada"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8558",title:"Aerodynamics",subtitle:null,isOpenForSubmission:!1,hash:"db7263fc198dfb539073ba0260a7f1aa",slug:"aerodynamics",bookSignature:"Mofid Gorji-Bandpy and Aly-Mousaad Aly",coverURL:"https://cdn.intechopen.com/books/images_new/8558.jpg",editors:[{id:"35542",title:"Prof.",name:"Mofid",middleName:null,surname:"Gorji-Bandpy",slug:"mofid-gorji-bandpy",fullName:"Mofid Gorji-Bandpy"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7847",title:"Medical Toxicology",subtitle:null,isOpenForSubmission:!1,hash:"db9b65bea093de17a0855a1b27046247",slug:"medical-toxicology",bookSignature:"Pınar Erkekoglu and Tomohisa Ogawa",coverURL:"https://cdn.intechopen.com/books/images_new/7847.jpg",editors:[{id:"109978",title:"Prof.",name:"Pınar",middleName:null,surname:"Erkekoglu",slug:"pinar-erkekoglu",fullName:"Pınar Erkekoglu"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"10432",title:"Casting Processes and Modelling of Metallic Materials",subtitle:null,isOpenForSubmission:!1,hash:"2c5c9df938666bf5d1797727db203a6d",slug:"casting-processes-and-modelling-of-metallic-materials",bookSignature:"Zakaria Abdallah and Nada Aldoumani",coverURL:"https://cdn.intechopen.com/books/images_new/10432.jpg",editors:[{id:"201670",title:"Dr.",name:"Zak",middleName:null,surname:"Abdallah",slug:"zak-abdallah",fullName:"Zak Abdallah"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],offset:12,limit:12,total:5238},hotBookTopics:{hotBooks:[],offset:0,limit:12,total:null},publish:{},publishingProposal:{success:null,errors:{}},books:{featuredBooks:[{type:"book",id:"10065",title:"Wavelet Theory",subtitle:null,isOpenForSubmission:!1,hash:"d8868e332169597ba2182d9b004d60de",slug:"wavelet-theory",bookSignature:"Somayeh Mohammady",coverURL:"https://cdn.intechopen.com/books/images_new/10065.jpg",editors:[{id:"109280",title:"Dr.",name:"Somayeh",middleName:null,surname:"Mohammady",slug:"somayeh-mohammady",fullName:"Somayeh Mohammady"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9644",title:"Glaciers and the Polar Environment",subtitle:null,isOpenForSubmission:!1,hash:"e8cfdc161794e3753ced54e6ff30873b",slug:"glaciers-and-the-polar-environment",bookSignature:"Masaki Kanao, Danilo Godone and Niccolò Dematteis",coverURL:"https://cdn.intechopen.com/books/images_new/9644.jpg",editors:[{id:"51959",title:"Dr.",name:"Masaki",middleName:null,surname:"Kanao",slug:"masaki-kanao",fullName:"Masaki Kanao"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9385",title:"Renewable Energy",subtitle:"Technologies and Applications",isOpenForSubmission:!1,hash:"a6b446d19166f17f313008e6c056f3d8",slug:"renewable-energy-technologies-and-applications",bookSignature:"Tolga Taner, Archana Tiwari and Taha Selim Ustun",coverURL:"https://cdn.intechopen.com/books/images_new/9385.jpg",editors:[{id:"197240",title:"Associate Prof.",name:"Tolga",middleName:null,surname:"Taner",slug:"tolga-taner",fullName:"Tolga Taner"}],equalEditorOne:{id:"186791",title:"Dr.",name:"Archana",middleName:null,surname:"Tiwari",slug:"archana-tiwari",fullName:"Archana Tiwari",profilePictureURL:"https://mts.intechopen.com/storage/users/186791/images/system/186791.jpg",biography:"Dr. Archana Tiwari is Associate Professor at Amity University, India. Her research interests include renewable sources of energy from microalgae and further utilizing the residual biomass for the generation of value-added products, bioremediation through microalgae and microbial consortium, antioxidative enzymes and stress, and nutraceuticals from microalgae. She has been working on algal biotechnology for the last two decades. She has published her research in many international journals and has authored many books and chapters with renowned publishing houses. She has also delivered talks as an invited speaker at many national and international conferences. Dr. Tiwari is the recipient of several awards including Researcher of the Year and Distinguished Scientist.",institutionString:"Amity University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Amity University",institutionURL:null,country:{name:"India"}}},equalEditorTwo:{id:"197609",title:"Prof.",name:"Taha Selim",middleName:null,surname:"Ustun",slug:"taha-selim-ustun",fullName:"Taha Selim Ustun",profilePictureURL:"https://mts.intechopen.com/storage/users/197609/images/system/197609.jpeg",biography:"Dr. Taha Selim Ustun received a Ph.D. in Electrical Engineering from Victoria University, Melbourne, Australia. He is a researcher with the Fukushima Renewable Energy Institute, AIST (FREA), where he leads the Smart Grid Cybersecurity Laboratory. Prior to that, he was a faculty member with the School of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. His current research interests include power systems protection, communication in power networks, distributed generation, microgrids, electric vehicle integration, and cybersecurity in smart grids. He serves on the editorial boards of IEEE Access, IEEE Transactions on Industrial Informatics, Energies, Electronics, Electricity, World Electric Vehicle and Information journals. Dr. Ustun is a member of the IEEE 2004 and 2800, IEC Renewable Energy Management WG 8, and IEC TC 57 WG17. He has been invited to run specialist courses in Africa, India, and China. He has delivered talks for the Qatar Foundation, the World Energy Council, the Waterloo Global Science Initiative, and the European Union Energy Initiative (EUEI). His research has attracted funding from prestigious programs in Japan, Australia, the European Union, and North America.",institutionString:"Fukushima Renewable Energy Institute, AIST (FREA)",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"National Institute of Advanced Industrial Science and Technology",institutionURL:null,country:{name:"Japan"}}},equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8985",title:"Natural Resources Management and Biological Sciences",subtitle:null,isOpenForSubmission:!1,hash:"5c2e219a6c021a40b5a20c041dea88c4",slug:"natural-resources-management-and-biological-sciences",bookSignature:"Edward R. Rhodes and Humood Naser",coverURL:"https://cdn.intechopen.com/books/images_new/8985.jpg",editors:[{id:"280886",title:"Prof.",name:"Edward R",middleName:null,surname:"Rhodes",slug:"edward-r-rhodes",fullName:"Edward R Rhodes"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9671",title:"Macrophages",subtitle:null,isOpenForSubmission:!1,hash:"03b00fdc5f24b71d1ecdfd75076bfde6",slug:"macrophages",bookSignature:"Hridayesh Prakash",coverURL:"https://cdn.intechopen.com/books/images_new/9671.jpg",editors:[{id:"287184",title:"Dr.",name:"Hridayesh",middleName:null,surname:"Prakash",slug:"hridayesh-prakash",fullName:"Hridayesh Prakash"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9313",title:"Clay Science and Technology",subtitle:null,isOpenForSubmission:!1,hash:"6fa7e70396ff10620e032bb6cfa6fb72",slug:"clay-science-and-technology",bookSignature:"Gustavo Morari Do Nascimento",coverURL:"https://cdn.intechopen.com/books/images_new/9313.jpg",editors:[{id:"7153",title:"Prof.",name:"Gustavo",middleName:null,surname:"Morari Do Nascimento",slug:"gustavo-morari-do-nascimento",fullName:"Gustavo Morari Do Nascimento"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9888",title:"Nuclear Power Plants",subtitle:"The Processes from the Cradle to the Grave",isOpenForSubmission:!1,hash:"c2c8773e586f62155ab8221ebb72a849",slug:"nuclear-power-plants-the-processes-from-the-cradle-to-the-grave",bookSignature:"Nasser Awwad",coverURL:"https://cdn.intechopen.com/books/images_new/9888.jpg",editors:[{id:"145209",title:"Prof.",name:"Nasser",middleName:"S",surname:"Awwad",slug:"nasser-awwad",fullName:"Nasser Awwad"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9027",title:"Human Blood Group Systems and Haemoglobinopathies",subtitle:null,isOpenForSubmission:!1,hash:"d00d8e40b11cfb2547d1122866531c7e",slug:"human-blood-group-systems-and-haemoglobinopathies",bookSignature:"Osaro Erhabor and Anjana Munshi",coverURL:"https://cdn.intechopen.com/books/images_new/9027.jpg",editors:[{id:"35140",title:null,name:"Osaro",middleName:null,surname:"Erhabor",slug:"osaro-erhabor",fullName:"Osaro Erhabor"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"10432",title:"Casting Processes and Modelling of Metallic Materials",subtitle:null,isOpenForSubmission:!1,hash:"2c5c9df938666bf5d1797727db203a6d",slug:"casting-processes-and-modelling-of-metallic-materials",bookSignature:"Zakaria Abdallah and Nada Aldoumani",coverURL:"https://cdn.intechopen.com/books/images_new/10432.jpg",editors:[{id:"201670",title:"Dr.",name:"Zak",middleName:null,surname:"Abdallah",slug:"zak-abdallah",fullName:"Zak Abdallah"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7841",title:"New Insights Into Metabolic Syndrome",subtitle:null,isOpenForSubmission:!1,hash:"ef5accfac9772b9e2c9eff884f085510",slug:"new-insights-into-metabolic-syndrome",bookSignature:"Akikazu Takada",coverURL:"https://cdn.intechopen.com/books/images_new/7841.jpg",editors:[{id:"248459",title:"Dr.",name:"Akikazu",middleName:null,surname:"Takada",slug:"akikazu-takada",fullName:"Akikazu Takada"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],latestBooks:[{type:"book",id:"9550",title:"Entrepreneurship",subtitle:"Contemporary Issues",isOpenForSubmission:!1,hash:"9b4ac1ee5b743abf6f88495452b1e5e7",slug:"entrepreneurship-contemporary-issues",bookSignature:"Mladen Turuk",coverURL:"https://cdn.intechopen.com/books/images_new/9550.jpg",editedByType:"Edited by",editors:[{id:"319755",title:"Prof.",name:"Mladen",middleName:null,surname:"Turuk",slug:"mladen-turuk",fullName:"Mladen Turuk"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10065",title:"Wavelet Theory",subtitle:null,isOpenForSubmission:!1,hash:"d8868e332169597ba2182d9b004d60de",slug:"wavelet-theory",bookSignature:"Somayeh Mohammady",coverURL:"https://cdn.intechopen.com/books/images_new/10065.jpg",editedByType:"Edited by",editors:[{id:"109280",title:"Dr.",name:"Somayeh",middleName:null,surname:"Mohammady",slug:"somayeh-mohammady",fullName:"Somayeh Mohammady"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9313",title:"Clay Science and Technology",subtitle:null,isOpenForSubmission:!1,hash:"6fa7e70396ff10620e032bb6cfa6fb72",slug:"clay-science-and-technology",bookSignature:"Gustavo Morari Do Nascimento",coverURL:"https://cdn.intechopen.com/books/images_new/9313.jpg",editedByType:"Edited by",editors:[{id:"7153",title:"Prof.",name:"Gustavo",middleName:null,surname:"Morari Do Nascimento",slug:"gustavo-morari-do-nascimento",fullName:"Gustavo Morari Do Nascimento"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9888",title:"Nuclear Power Plants",subtitle:"The Processes from the Cradle to the Grave",isOpenForSubmission:!1,hash:"c2c8773e586f62155ab8221ebb72a849",slug:"nuclear-power-plants-the-processes-from-the-cradle-to-the-grave",bookSignature:"Nasser Awwad",coverURL:"https://cdn.intechopen.com/books/images_new/9888.jpg",editedByType:"Edited by",editors:[{id:"145209",title:"Prof.",name:"Nasser",middleName:"S",surname:"Awwad",slug:"nasser-awwad",fullName:"Nasser Awwad"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8098",title:"Resources of Water",subtitle:null,isOpenForSubmission:!1,hash:"d251652996624d932ef7b8ed62cf7cfc",slug:"resources-of-water",bookSignature:"Prathna Thanjavur Chandrasekaran, Muhammad Salik Javaid, Aftab Sadiq",coverURL:"https://cdn.intechopen.com/books/images_new/8098.jpg",editedByType:"Edited by",editors:[{id:"167917",title:"Dr.",name:"Prathna",middleName:null,surname:"Thanjavur Chandrasekaran",slug:"prathna-thanjavur-chandrasekaran",fullName:"Prathna Thanjavur Chandrasekaran"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9644",title:"Glaciers and the Polar Environment",subtitle:null,isOpenForSubmission:!1,hash:"e8cfdc161794e3753ced54e6ff30873b",slug:"glaciers-and-the-polar-environment",bookSignature:"Masaki Kanao, Danilo Godone and Niccolò Dematteis",coverURL:"https://cdn.intechopen.com/books/images_new/9644.jpg",editedByType:"Edited by",editors:[{id:"51959",title:"Dr.",name:"Masaki",middleName:null,surname:"Kanao",slug:"masaki-kanao",fullName:"Masaki Kanao"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10432",title:"Casting Processes and Modelling of Metallic Materials",subtitle:null,isOpenForSubmission:!1,hash:"2c5c9df938666bf5d1797727db203a6d",slug:"casting-processes-and-modelling-of-metallic-materials",bookSignature:"Zakaria Abdallah and Nada Aldoumani",coverURL:"https://cdn.intechopen.com/books/images_new/10432.jpg",editedByType:"Edited by",editors:[{id:"201670",title:"Dr.",name:"Zak",middleName:null,surname:"Abdallah",slug:"zak-abdallah",fullName:"Zak Abdallah"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9671",title:"Macrophages",subtitle:null,isOpenForSubmission:!1,hash:"03b00fdc5f24b71d1ecdfd75076bfde6",slug:"macrophages",bookSignature:"Hridayesh Prakash",coverURL:"https://cdn.intechopen.com/books/images_new/9671.jpg",editedByType:"Edited by",editors:[{id:"287184",title:"Dr.",name:"Hridayesh",middleName:null,surname:"Prakash",slug:"hridayesh-prakash",fullName:"Hridayesh Prakash"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8415",title:"Extremophilic Microbes and Metabolites",subtitle:"Diversity, Bioprospecting and Biotechnological Applications",isOpenForSubmission:!1,hash:"93e0321bc93b89ff73730157738f8f97",slug:"extremophilic-microbes-and-metabolites-diversity-bioprospecting-and-biotechnological-applications",bookSignature:"Afef Najjari, Ameur Cherif, Haïtham Sghaier and Hadda Imene Ouzari",coverURL:"https://cdn.intechopen.com/books/images_new/8415.jpg",editedByType:"Edited by",editors:[{id:"196823",title:"Dr.",name:"Afef",middleName:null,surname:"Najjari",slug:"afef-najjari",fullName:"Afef Najjari"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9731",title:"Oxidoreductase",subtitle:null,isOpenForSubmission:!1,hash:"852e6f862c85fc3adecdbaf822e64e6e",slug:"oxidoreductase",bookSignature:"Mahmoud Ahmed Mansour",coverURL:"https://cdn.intechopen.com/books/images_new/9731.jpg",editedByType:"Edited by",editors:[{id:"224662",title:"Prof.",name:"Mahmoud Ahmed",middleName:null,surname:"Mansour",slug:"mahmoud-ahmed-mansour",fullName:"Mahmoud Ahmed Mansour"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},subject:{topic:{id:"1134",title:"Population Health",slug:"population-health",parent:{title:"Public Health",slug:"medicine-public-health"},numberOfBooks:7,numberOfAuthorsAndEditors:241,numberOfWosCitations:118,numberOfCrossrefCitations:81,numberOfDimensionsCitations:177,videoUrl:null,fallbackUrl:null,description:null},booksByTopicFilter:{topicSlug:"population-health",sort:"-publishedDate",limit:12,offset:0},booksByTopicCollection:[{type:"book",id:"6142",title:"Family Planning",subtitle:null,isOpenForSubmission:!1,hash:"4993c79cffba3126a9ca1ef7c9902c7e",slug:"family-planning",bookSignature:"Zouhair O. Amarin",coverURL:"https://cdn.intechopen.com/books/images_new/6142.jpg",editedByType:"Edited by",editors:[{id:"101551",title:"Prof.",name:"Zouhair",middleName:null,surname:"Amarin",slug:"zouhair-amarin",fullName:"Zouhair Amarin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5483",title:"Adiposity",subtitle:"Epidemiology and Treatment Modalities",isOpenForSubmission:!1,hash:"5f19b6a0755b8a29538e3b2043d4a854",slug:"adiposity-epidemiology-and-treatment-modalities",bookSignature:"Jan Oxholm Gordeladze",coverURL:"https://cdn.intechopen.com/books/images_new/5483.jpg",editedByType:"Edited by",editors:[{id:"36345",title:"Prof.",name:"Jan",middleName:"Oxholm",surname:"Gordeladze",slug:"jan-gordeladze",fullName:"Jan Gordeladze"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5378",title:"The Epidemiology and Ecology of Leishmaniasis",subtitle:null,isOpenForSubmission:!1,hash:"862e269e0512a4763bba54d355c3c44f",slug:"the-epidemiology-and-ecology-of-leishmaniasis",bookSignature:"David Claborn",coverURL:"https://cdn.intechopen.com/books/images_new/5378.jpg",editedByType:"Edited by",editors:[{id:"169536",title:"Dr.",name:"David",middleName:null,surname:"Claborn",slug:"david-claborn",fullName:"David Claborn"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5287",title:"Epidemiology of Communicable and Non-Communicable Diseases",subtitle:"Attributes of Lifestyle and Nature on Humankind",isOpenForSubmission:!1,hash:"c8ce64cf0b96dce4c16042b2982ef5bb",slug:"epidemiology-of-communicable-and-non-communicable-diseases-attributes-of-lifestyle-and-nature-on-humankind",bookSignature:"Fyson H. Kasenga",coverURL:"https://cdn.intechopen.com/books/images_new/5287.jpg",editedByType:"Edited by",editors:[{id:"86725",title:"Dr.",name:"Fyson",middleName:"Hanania",surname:"Kasenga",slug:"fyson-kasenga",fullName:"Fyson Kasenga"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2140",title:"Epidemiology Insights",subtitle:null,isOpenForSubmission:!1,hash:"30ed18695c28d4af7aa8fe536a765829",slug:"epidemiology-insights",bookSignature:"Maria de Lourdes Ribeiro de Souza da Cunha",coverURL:"https://cdn.intechopen.com/books/images_new/2140.jpg",editedByType:"Edited by",editors:[{id:"87931",title:"Dr.",name:"Maria De Lourdes",middleName:null,surname:"Ribeiro De Souza Da Cunha",slug:"maria-de-lourdes-ribeiro-de-souza-da-cunha",fullName:"Maria De Lourdes Ribeiro De Souza Da Cunha"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"313",title:"Health Management",subtitle:"Different Approaches and Solutions",isOpenForSubmission:!1,hash:"50844b8973e93e755acd2e5a950f2766",slug:"health-management-different-approaches-and-solutions",bookSignature:"Krzysztof Śmigórski",coverURL:"https://cdn.intechopen.com/books/images_new/313.jpg",editedByType:"Edited by",editors:[{id:"12528",title:"Dr.",name:"Krzysztof",middleName:null,surname:"Smigorski",slug:"krzysztof-smigorski",fullName:"Krzysztof Smigorski"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5115",title:"Health Management",subtitle:null,isOpenForSubmission:!1,hash:"6af1c688ae4cf514c5c3ed6e801f6725",slug:"health-management",bookSignature:"Krzysztof Smigorski",coverURL:"https://cdn.intechopen.com/books/images_new/5115.jpg",editedByType:"Edited by",editors:[{id:"12528",title:"Dr.",name:"Krzysztof",middleName:null,surname:"Smigorski",slug:"krzysztof-smigorski",fullName:"Krzysztof Smigorski"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:7,mostCitedChapters:[{id:"35769",doi:"10.5772/31746",title:"Epidemiology of Lymphoid Malignancy in Asia",slug:"epidemiology-of-lymphoid-malignancy-in-asia",totalDownloads:4675,totalCrossrefCites:7,totalDimensionsCites:9,book:{slug:"epidemiology-insights",title:"Epidemiology Insights",fullTitle:"Epidemiology Insights"},signatures:"Zahra Mozaheb",authors:[{id:"88507",title:"Prof.",name:"Zahra",middleName:null,surname:"Mozaheb",slug:"zahra-mozaheb",fullName:"Zahra Mozaheb"}]},{id:"24997",doi:"10.5772/21387",title:"Human Walking Analysis, Evaluation and Classification Based on Motion Capture System",slug:"human-walking-analysis-evaluation-and-classification-based-on-motion-capture-system",totalDownloads:2964,totalCrossrefCites:6,totalDimensionsCites:8,book:{slug:"health-management-different-approaches-and-solutions",title:"Health Management",fullTitle:"Health Management - Different Approaches and Solutions"},signatures:"Bofeng Zhang, Susu Jiang, Ke Yan and Daming Wei",authors:[{id:"42911",title:"Prof.",name:"Daming",middleName:null,surname:"Wei",slug:"daming-wei",fullName:"Daming Wei"},{id:"52921",title:"Prof.",name:"Bofeng",middleName:null,surname:"Zhang",slug:"bofeng-zhang",fullName:"Bofeng Zhang"},{id:"52924",title:"Ms",name:"Susu",middleName:null,surname:"Jiang",slug:"susu-jiang",fullName:"Susu Jiang"},{id:"52937",title:"Mr",name:"Ke",middleName:null,surname:"Yan",slug:"ke-yan",fullName:"Ke Yan"}]},{id:"24998",doi:"10.5772/22790",title:"The Role of Mass Media Communication in Public Health",slug:"the-role-of-mass-media-communication-in-public-health",totalDownloads:10305,totalCrossrefCites:5,totalDimensionsCites:8,book:{slug:"health-management-different-approaches-and-solutions",title:"Health Management",fullTitle:"Health Management - Different Approaches and Solutions"},signatures:"Daniel Catalán-Matamoros",authors:[{id:"49198",title:"Dr.",name:"Daniel",middleName:null,surname:"Catalan-Matamoros",slug:"daniel-catalan-matamoros",fullName:"Daniel Catalan-Matamoros"}]}],mostDownloadedChaptersLast30Days:[{id:"58916",title:"Factors Affecting the Attitudes of Women toward Family Planning",slug:"factors-affecting-the-attitudes-of-women-toward-family-planning",totalDownloads:5944,totalCrossrefCites:4,totalDimensionsCites:8,book:{slug:"family-planning",title:"Family Planning",fullTitle:"Family Planning"},signatures:"Nazli Sensoy, Yasemin Korkut, Selcuk Akturan, Mehmet Yilmaz,\nCanan Tuz and Bilge Tuncel",authors:[{id:"216377",title:"Prof.",name:"Nazli",middleName:null,surname:"Sensoy",slug:"nazli-sensoy",fullName:"Nazli Sensoy"},{id:"216589",title:"Dr.",name:"Yasemin",middleName:null,surname:"Korkut",slug:"yasemin-korkut",fullName:"Yasemin Korkut"},{id:"216595",title:"Dr.",name:"Selcuk",middleName:null,surname:"Akturan",slug:"selcuk-akturan",fullName:"Selcuk Akturan"},{id:"216596",title:"Dr.",name:"Canan",middleName:null,surname:"Tuz",slug:"canan-tuz",fullName:"Canan Tuz"},{id:"216598",title:"Dr.",name:"Bilge",middleName:null,surname:"Tuncel",slug:"bilge-tuncel",fullName:"Bilge Tuncel"},{id:"216599",title:"Dr.",name:"Mehmet",middleName:null,surname:"Yilmaz",slug:"mehmet-yilmaz",fullName:"Mehmet Yilmaz"}]},{id:"58297",title:"Family Planning Services in Africa: The Successes and Challenges",slug:"family-planning-services-in-africa-the-successes-and-challenges",totalDownloads:1195,totalCrossrefCites:3,totalDimensionsCites:5,book:{slug:"family-planning",title:"Family Planning",fullTitle:"Family Planning"},signatures:"Alhaji A Aliyu",authors:[{id:"217688",title:"Prof.",name:"Alhaji A",middleName:null,surname:"Aliyu",slug:"alhaji-a-aliyu",fullName:"Alhaji A Aliyu"}]},{id:"53472",title:"Nutrition Labelling: Educational Tool for Reducing Risks of Obesity-Related Non-communicable Diseases",slug:"nutrition-labelling-educational-tool-for-reducing-risks-of-obesity-related-non-communicable-diseases",totalDownloads:1224,totalCrossrefCites:1,totalDimensionsCites:2,book:{slug:"adiposity-epidemiology-and-treatment-modalities",title:"Adiposity",fullTitle:"Adiposity - Epidemiology and Treatment Modalities"},signatures:"Visith Chavasit, Wantanee Kriengsinyos, Mayuree Ditmetharoj,\nManasuwee Phaichamanan, Kangsadan Singsoong, Prapaisri\nSirichakwal and Araya Rojjanawanicharkorn",authors:[{id:"191830",title:"Prof.",name:"Visith",middleName:null,surname:"Chavasit",slug:"visith-chavasit",fullName:"Visith Chavasit"}]},{id:"53594",title:"Application of the Eco-Epidemiological Method in the Study of Leishmaniasis Transmission Foci",slug:"application-of-the-eco-epidemiological-method-in-the-study-of-leishmaniasis-transmission-foci",totalDownloads:1044,totalCrossrefCites:0,totalDimensionsCites:2,book:{slug:"the-epidemiology-and-ecology-of-leishmaniasis",title:"The Epidemiology and Ecology of Leishmaniasis",fullTitle:"The Epidemiology and Ecology of Leishmaniasis"},signatures:"Iván D. Vélez, Lina M. Carrillo, Horacio Cadena, Carlos Muskus and\nSara M. Robledo",authors:[{id:"187783",title:"Dr.",name:"Sara M.",middleName:null,surname:"Robledo",slug:"sara-m.-robledo",fullName:"Sara M. Robledo"},{id:"189117",title:"Dr.",name:"Ivan D.",middleName:null,surname:"Velez",slug:"ivan-d.-velez",fullName:"Ivan D. Velez"},{id:"189118",title:"Dr.",name:"Lina M.",middleName:null,surname:"Carrillo",slug:"lina-m.-carrillo",fullName:"Lina M. Carrillo"},{id:"189119",title:"Dr.",name:"Horacio",middleName:null,surname:"Cadena",slug:"horacio-cadena",fullName:"Horacio Cadena"},{id:"189120",title:"Dr.",name:"Carlos",middleName:null,surname:"Muskus",slug:"carlos-muskus",fullName:"Carlos Muskus"}]},{id:"52377",title:"Lay Theories of Obesity: Causes and Consequences",slug:"lay-theories-of-obesity-causes-and-consequences",totalDownloads:1691,totalCrossrefCites:2,totalDimensionsCites:3,book:{slug:"adiposity-epidemiology-and-treatment-modalities",title:"Adiposity",fullTitle:"Adiposity - Epidemiology and Treatment Modalities"},signatures:"Paul H. Thibodeau and Stephen J. Flusberg",authors:[{id:"190818",title:"Prof.",name:"Paul",middleName:null,surname:"Thibodeau",slug:"paul-thibodeau",fullName:"Paul Thibodeau"},{id:"190820",title:"Prof.",name:"Stephen",middleName:null,surname:"Flusberg",slug:"stephen-flusberg",fullName:"Stephen Flusberg"}]},{id:"50982",title:"Epidemiology of Vitamin B12 Deficiency",slug:"epidemiology-of-vitamin-b12-deficiency",totalDownloads:1687,totalCrossrefCites:1,totalDimensionsCites:3,book:{slug:"epidemiology-of-communicable-and-non-communicable-diseases-attributes-of-lifestyle-and-nature-on-humankind",title:"Epidemiology of Communicable and Non-Communicable Diseases",fullTitle:"Epidemiology of Communicable and Non-Communicable Diseases - Attributes of Lifestyle and Nature on Humankind"},signatures:"Tekin Guney, Aysun Senturk Yikilmaz and Imdat Dilek",authors:[{id:"182835",title:"M.D.",name:"Tekin",middleName:null,surname:"Guney",slug:"tekin-guney",fullName:"Tekin Guney"},{id:"183364",title:"Dr.",name:"Aysun",middleName:null,surname:"Senturk Yikilmaz",slug:"aysun-senturk-yikilmaz",fullName:"Aysun Senturk Yikilmaz"},{id:"183366",title:"Prof.",name:"Imdat",middleName:null,surname:"Dilek",slug:"imdat-dilek",fullName:"Imdat Dilek"}]},{id:"52888",title:"Dietary and Hormonal Factors Involved in Healthy or Unhealthy Visceral Adipose Tissue Expansion",slug:"dietary-and-hormonal-factors-involved-in-healthy-or-unhealthy-visceral-adipose-tissue-expansion",totalDownloads:1180,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"adiposity-epidemiology-and-treatment-modalities",title:"Adiposity",fullTitle:"Adiposity - Epidemiology and Treatment Modalities"},signatures:"María Guillermina Zubiría, Ana Alzamendi, Luisina Ongaro, Eduardo\nSpinedi and Andrés Giovambattista",authors:[{id:"191011",title:"Dr.",name:"Andrés",middleName:null,surname:"Giovambattista",slug:"andres-giovambattista",fullName:"Andrés Giovambattista"},{id:"191724",title:"Dr.",name:"María Guillermina",middleName:null,surname:"Zubiría",slug:"maria-guillermina-zubiria",fullName:"María Guillermina Zubiría"},{id:"191729",title:"Dr.",name:"Ana",middleName:null,surname:"Alzamendi",slug:"ana-alzamendi",fullName:"Ana Alzamendi"},{id:"191731",title:"Dr.",name:"Luisina",middleName:null,surname:"Ongaro Gambino",slug:"luisina-ongaro-gambino",fullName:"Luisina Ongaro Gambino"},{id:"191733",title:"Dr.",name:"Eduardo",middleName:null,surname:"Spinedi",slug:"eduardo-spinedi",fullName:"Eduardo Spinedi"}]},{id:"53011",title:"Multimodal Lifestyle Intervention: Outlines and Outcomes",slug:"multimodal-lifestyle-intervention-outlines-and-outcomes",totalDownloads:1095,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"adiposity-epidemiology-and-treatment-modalities",title:"Adiposity",fullTitle:"Adiposity - Epidemiology and Treatment Modalities"},signatures:"Mahmoud M. A. Abulmeaty",authors:[{id:"190647",title:"Dr.",name:"Mahmoud",middleName:null,surname:"Abulmeaty",slug:"mahmoud-abulmeaty",fullName:"Mahmoud Abulmeaty"}]},{id:"58267",title:"Birth Control and Family Planning Using Intrauterine Devices (IUDs)",slug:"birth-control-and-family-planning-using-intrauterine-devices-iuds-",totalDownloads:1403,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"family-planning",title:"Family Planning",fullTitle:"Family Planning"},signatures:"Iliescu Dominic Gabriel, Ștefania Tudorache, Simona Vlădăreanu,\nNuți Daniela Oprescu, Maria Cezara Mureșan, Roxana Cristina\nDrăgușin and Iuliana Ceaușu",authors:[{id:"209081",title:"Dr.",name:"Stefania",middleName:null,surname:"Tudorache",slug:"stefania-tudorache",fullName:"Stefania Tudorache"},{id:"212459",title:"Dr.",name:"Dominic",middleName:null,surname:"Iliescu",slug:"dominic-iliescu",fullName:"Dominic Iliescu"},{id:"212490",title:"Dr.",name:"Dragusin",middleName:null,surname:"Roxana",slug:"dragusin-roxana",fullName:"Dragusin Roxana"},{id:"215126",title:"Prof.",name:"Simona",middleName:null,surname:"Vladareanu",slug:"simona-vladareanu",fullName:"Simona Vladareanu"},{id:"215135",title:"Prof.",name:"Iuliana",middleName:null,surname:"Ceausu",slug:"iuliana-ceausu",fullName:"Iuliana Ceausu"},{id:"216852",title:"Prof.",name:"Dana",middleName:null,surname:"Oprescu",slug:"dana-oprescu",fullName:"Dana Oprescu"},{id:"230814",title:"Assistant Prof.",name:"Maria Cezara",middleName:null,surname:"Muresan",slug:"maria-cezara-muresan",fullName:"Maria Cezara Muresan"}]},{id:"58484",title:"Male Contraceptives",slug:"male-contraceptives",totalDownloads:576,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"family-planning",title:"Family Planning",fullTitle:"Family Planning"},signatures:"Eka Rusdianto Gunardi and Yohanes Handoko",authors:[{id:"217138",title:"Dr.",name:"Eka Rusdianto",middleName:null,surname:"Gunardi",slug:"eka-rusdianto-gunardi",fullName:"Eka Rusdianto Gunardi"},{id:"217139",title:"M.D.",name:"Yohanes",middleName:null,surname:"Handoko",slug:"yohanes-handoko",fullName:"Yohanes Handoko"}]}],onlineFirstChaptersFilter:{topicSlug:"population-health",limit:3,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[{type:"book",id:"10176",title:"Microgrids and Local Energy Systems",subtitle:null,isOpenForSubmission:!0,hash:"c32b4a5351a88f263074b0d0ca813a9c",slug:null,bookSignature:"Prof. Nick Jenkins",coverURL:"https://cdn.intechopen.com/books/images_new/10176.jpg",editedByType:null,editors:[{id:"55219",title:"Prof.",name:"Nick",middleName:null,surname:"Jenkins",slug:"nick-jenkins",fullName:"Nick Jenkins"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],offset:8,limit:8,total:1},route:{name:"profile.detail",path:"/profiles/193065/maoxi-tian",hash:"",query:{},params:{id:"193065",slug:"maoxi-tian"},fullPath:"/profiles/193065/maoxi-tian",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()